A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments

Yeste, Patricio; García-Valdecasas Ojeda, Matilde; Gámiz-Fortis, Sonia R.; Castro-Díez, Yolanda; Bronstert, Axel; Esteban-Parra, María Jesús

doi:https://doi.org/10.5194/hess-2024-57

Preprints

https://doi.org/10.5194/hess-2024-57

Preprints

08 Mar 2024

| 08 Mar 2024

Status: a revised version of this preprint was accepted for the journal HESS and is expected to appear here in due course.

A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments

Patricio Yeste, Matilde García-Valdecasas Ojeda, Sonia R. Gámiz-Fortis, Yolanda Castro-Díez, Axel Bronstert, and María Jesús Esteban-Parra

Abstract. The simultaneous incorporation of streamflow and evaporation data into sensitivity analysis and calibration approaches has a great potential to improve the representation of hydrologic processes in modelling frameworks. This work aims to investigate the capabilities of the Variable Infiltration Capacity (VIC) model in a large-sample application focused on the joint integration of streamflow and evaporation data for 189 headwater catchments located in Spain. The study has been articulated into three parts: (1) a regional sensitivity analysis for a total of 20 soil, routing and vegetation parameters to select the most important parameters conducive to an adequate representation of the streamflow and evaporation dynamics; (2) a two-fold calibration approach against daily streamflow and monthly evaporation data based on the previous parameter selection for VIC, and (3) an evaluation of model performance based on a benchmark comparison against a well-established hydrologic model for the Spanish domain and a cross-validation test using multiple meteorological datasets to assess the generalizability of the calibrated parameters. The regional sensitivity analysis revealed that only two vegetation parameters – namely, the leaf area index and the minimum stomatal resistance – were sufficient to improve the performance of VIC for evaporation. These parameters were added to the soil and routing parameter during the calibration stage. Results from the two calibration experiments suggested that, while the streamflow performance remained close in both cases, the evaporation performance was highly improved if the objectives for streamflow and evaporation were combined into a single composite function during optimization. The VIC model outperfomed the reference benchmark and the independent meteorological datasets yielded a slight to moderate loss in model performance depending on the calibration experiment considered. This investigation will help gain a better understanding of the hydrology of the Spanish catchments and will help prepare the ground for a fully gridded implementation of the VIC model in Spain.

Received: 24 Feb 2024 – Discussion started: 08 Mar 2024

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.

Download & links

Patricio Yeste, Matilde García-Valdecasas Ojeda, Sonia R. Gámiz-Fortis, Yolanda Castro-Díez, Axel Bronstert, and María Jesús Esteban-Parra

Status: closed

RC1:
'Comment on hess-2024-57', Anonymous Referee #1, 28 Jun 2024
This study investigates the capacity of the VIC model to integrate streamflow and evaporation data in a large-sample application, based on simulations for 189 headwater catchments in Spain. Utilizing multiple datasets to improve model performance is an important aspect of hydrological modeling research, making this paper potential for publication in HESS. However, some important issues are not discussed adequately, and there is room for improvement. Consequently, I recommend a major revision before publication. My major suggestions are as follows:
Given the general imbalances in Q, E, and P, I question whether it is reasonable to use the evaporation dataset to evaluate the model directly. This brings another question: what is the most important signature provided by the evaporation data? Is it the evaporation amount or the temporal variation of evaporation? If the key information provided by the evaporation data is the amount, we can expect that the E simulation obtained by Q-only calibration will be good in the catchments with Q+E/P close to 1, and the NSEQ obtained by Q-E calibration will be lower than that from Q-only calibration (perhaps the authors can test whether the results indeed show this characteristic). Otherwise, if the key information provided by the evaporation data is the variation, conducting a bias correction on E data based on water balance before calibration makes more sense.

An important role of adopting multiple datasets is to reduce equifinality, i.e., to reject some parameters that perform well in the simulation of only one objective. However, the authors didn’t address this issue, stopping at presenting good simulations for streamflow and evaporation. I encourage the authors to discuss the value of the evaporation dataset in reducing equifinality. A potential way to address this issue is to analyze the sensitivity of the integrated objective function to the parameters and compare it with the sensitivity of NSEQ.

Other minor and moderate issues:
-Data:
There are negative records at some stations, which can be attributed to the reservoir. So a question is whether the influence of the reservoir on streamflow is significant and whether the reservoir is simulated in the model. To my knowledge, some rivers significantly influenced by reservoirs have an extremely even interannual streamflow distribution, which is impossible to reproduce if the reservoir is not considered in the model.
-Methods:
The model performance was evaluated using NSE and its decomposition into γ, α, and β. However, to my understanding, γ, α, and β are the decomposition of KGE rather than NSE. NSE actually only quantifies the bias characteristic. I suggest the authors modify this expression. Additionally, please add the equations for these three metrics.
I am a little confused about the SST test. If I understand correctly, this seems to be the common practice in model calibration, i.e., to divide calibration and evaluation periods, with a warm-up period in each. Please correct me if I am wrong, but if this is indeed the common practice, I suggest not referring to it using a special term.
-Sensitivity Analysis:
The authors calculate the sensitivity of model performance to parameters and analyze the correlation between sensitivity and physiographic hydroclimatic characteristics. I think it would be interesting to discuss the mechanism behind this correlation, e.g., why NSEQ is more sensitive to rout1 in catchments with larger precipitation. This can also provide guidance on selecting sensitivity parameters in regions with different conditions. I encourage the authors to delve deeper into this analysis or discussion. Currently, this is only discussed by comparing with other studies, without addressing the underlying reasons.
-The Y-axis of Figure 7 is incorrect.
Citation: https://doi.org/10.5194/hess-2024-57-RC1
- AC1: 'Reply on RC1', Patricio Yeste, 16 Jul 2024
  
  Dear reviewer,
  Our responses can be found in the attached PDF document.
  On behalf of all the authors,
  Patricio Yeste
  
  Citation: https://doi.org/10.5194/hess-2024-57-AC1
RC2:
'Comment on hess-2024-57', Ilhan Özgen-Xian, 04 Jul 2024

Summary. This article investigates how using evapotranspiration in addition to streamflow data during the calibration process affects the model performance across 189 Spanish catchments. The Variable Infiltration Capacity (VIC) model, a semi-distributed hydrological model, is used for modelling. For the calibration of evapotranspiration fluxes, leaf area index and minimal stomatal conductivity were found to be the most sensitive parameters. Apart from the vegetation parameterisation, soil parameters were found to have a large effect on model results. The VIC model is run for 20 years (with a spin-up period of 10 years) using different targets for the calibration. Results are analysed and discussed with a focus on model sensitivity to calibration target.
Assessment. The subject of this article is interesting. Indeed, large sample and large scale hydrological modelling is becoming more feasible, due to improvements in computing technology. In this sense, the topic is timely and of interest. The manuscript is well written and easy to follow. I think the modelling work is substantial and the work is suitable to be published in HESS. I have some questions that I would like the authors to address. These are listed below. I recommend minor revision before publication.
Comment on large sample modelling in this work.
The use of large samples in hydrology is very interesting. Especially when combined with a comparative analysis, the large sample size can help us to discover relations among processes and generate hydrological insights. While this work made use of data from a large number of catchments, the discussion of the results were focused on quite technical issues of model structure and calibration. I do not think this is bad, in fact, these are important topics. But I wonder whether the conclusion in the abstract, i.e. "This investigation will help gain a better understanding of the hydrology of the Spanish catchments and will help prepare the ground for a fully gridded implementation of the VIC model in Spain." still holds. Perhaps in the conclusions, the authors could provide some insights of the hydrology of Spanish catchments they gained in this study. Or perhaps this manuscript focuses on model sensitivity, in this case, this should be better reflected in the abstract.
Questions.
1. Here is my understanding of the modelling work, please correct me if I am wrong: The VIC model is a semi-distributed hydrological model in the sense that no horizontal fluxes are computed between individual grid cells. VIC is set up for entire Spain, thus, all catchments included in this studies data set are represented in the model. Model calibration is done by adjusting model parameters in each grid cell individually.

2. How were soil hydraulic properties aggregated from 1 km to 5 km?

3. Streamflow and evapotranspiration processes have distinct time scales. Are model results of the same variable at different temporal resolution, for example, daily vs. subdaily stream flow, sensitive to different model parameters?

4. Does calibration with only stream flow, only evapotranspiration, and both of them combined result in significantly different model parameterisations?

5. Using the model results in this study, can the relations between the Nash-Sutcliffe efficiencies and model parameters reported in Fig. 6 be interpreted from a hydrological point of view?

6. On page 11, it is reported that a simultaneous calibration with both streamflow and evapotranspiration results in a degradation of model performance. Hydrological models that have been calibrated against more than one type of data often display a greater generalisation capability to changing climate conditions. Can this be seen for the VIC model in the simulated time frame in this study? Is this what is discussed in the last paragraph of Sec. 5.2?

7. Sec. 3.3: What climate forcing was used to spin-up the simulation? From the corresponding 10 years preceeding the simulation period?

Citation: https://doi.org/10.5194/hess-2024-57-RC2
- AC2: 'Reply on RC2', Patricio Yeste, 16 Jul 2024
  
  Dear reviewer,
  Our responses can be found in the attached PDF document.
  On behalf of all the authors,
  Patricio Yeste
  
  Citation: https://doi.org/10.5194/hess-2024-57-AC2
RC3:
'Comment on hess-2024-57', Anonymous Referee #3, 05 Jul 2024

Dear Authors,
thank you very much for your work. I think the work is interesting but I have some concerns:
1- introductioni miss literature review, eg.
a) what about the intercomparision porject MOPEX
b) what about prediction in ungauged catchments
Please, see key references, also for work that has been performend in Spain
Prieto, C., Le Vine, N., Kavetski, D., Fenicia, F., Scheidegger, A., & Vitolo, C. (2022). An exploration of Bayesian identification of dominant hydrological mechanisms in ungauged catchments. Water Resources Research, 58, e2021WR030705. https://doi.org/10.1029/2021WR030705
Prieto, C., Kavetski, D., Le Vine, N., Álvarez, C., & Medina, R. (2021). Identification of dominant hydrological mechanisms using Bayesian inference, multiple statistical hypothesis testing, and flexible models. Water Resources Research, 57, e2020WR028338. https://doi.org/10.1029/2020WR028338
Prieto, C., Le Vine, N., Kavetski, D., García, E., & Medina, R. (2019). Flow prediction in ungauged catchments using probabilistic random forests regionalization and new statistical adequacy tests. Water Resources Research, 55, 4364–4392. https://doi.org/10.1029/2018WR023254

Almeida, S., Le Vine, N., McIntyre, N., Wagener, T., and Buytaert, W.: Accounting for dependencies in regionalized signatures for predictions in ungauged catchments, Hydrol. Earth Syst. Sci., 20, 887–901, https://doi.org/10.5194/hess-20-887-2016, 2016.
Addor, N., Nearing, G., Prieto, C., Newman, A. J., Le Vine, N., & Clark, M. P. (2018). A ranking of hydrological signatures based on their predictability in space. Water Resources Research, 54, 8792–8812. https://doi.org/10.1029/2018WR022606
again, line 31: "accross climates" i suggest you to have a look at Addor et al., 2018
Addor, N., Nearing, G., Prieto, C., Newman, A. J., Le Vine, N., & Clark, M. P. (2018). A ranking of hydrological signatures based on their predictability in space. Water Resources Research, 54, 8792–8812. https://doi.org/10.1029/2018WR022606

Line: 40 "evaluation and benchmarking". i suggest you to have a look at Prieto el al., 2021;2022
Prieto, C., Le Vine, N., Kavetski, D., Fenicia, F., Scheidegger, A., & Vitolo, C. (2022). An exploration of Bayesian identification of dominant hydrological mechanisms in ungauged catchments. Water Resources Research, 58, e2021WR030705. https://doi.org/10.1029/2021WR030705
Prieto, C., Kavetski, D., Le Vine, N., Álvarez, C., & Medina, R. (2021). Identification of dominant hydrological mechanisms using Bayesian inference, multiple statistical hypothesis testing, and flexible models. Water Resources Research, 57, e2020WR028338. https://doi.org/10.1029/2020WR028338
Line 43: parameter regionalization techniques, i recommend u to have a look at
Prieto, C., Le Vine, N., Kavetski, D., García, E., & Medina, R. (2019). Flow prediction in ungauged catchments using probabilistic random forests regionalization and new statistical adequacy tests. Water Resources Research, 55, 4364–4392. https://doi.org/10.1029/2018WR023254

Almeida, S., Le Vine, N., McIntyre, N., Wagener, T., and Buytaert, W.: Accounting for dependencies in regionalized signatures for predictions in ungauged catchments, Hydrol. Earth Syst. Sci., 20, 887–901, https://doi.org/10.5194/hess-20-887-2016, 2016.
line 64: "there is an inreasing tendency towards aridity conditions": what is the difference for different catchments in spain
Study area and data:
line 80: specify the northern districts (there are "many", eg, aguas de galicia, chc, ura, aca)
Also, in section 2 i recomend to provide the range of mean annual precipitation, mean annual flow, mean annual potential evaporanspiration and rainfall runoff coefficient accross catchments and maybe per river basin district in the text . This is to guide the reader.
Line 205: you are using SIMPA as benchmark which we know is a very simple model wrt vic. Maybe, include the pros and cons or similarities and differences as most of the readers won't know what SIMPA is
Discussion:
I miss 1) talking about uncertainty, 2) talking about model structure error, and 3) talking or comparing (which would go to the methods) with a more well stablished model, e.g. gr4j, even if simpa is used as benchmar. So that you would have simpa, vic and gr4j.
I also miss, to compare with other models and results that were run at tdaily time scale in spain, e.g. look at URA
Conclusions:
"The soil and routing parameters were reveled as the most important parameters".
Could you add in which type of catchments were most and least important?

Once again, thank you very much for your work
In case the editor asks for a revised version of the manuscript, i am very happy to serve as reviewer of the revised version
All the Best,
Reviewer

Citation: https://doi.org/10.5194/hess-2024-57-RC3
- AC3: 'Reply on RC3', Patricio Yeste, 16 Jul 2024
  
  Dear reviewer,
  Our responses can be found in the attached PDF document.
  On behalf of all the authors,
  Patricio Yeste
  
  Citation: https://doi.org/10.5194/hess-2024-57-AC3

Status: closed

RC1:
'Comment on hess-2024-57', Anonymous Referee #1, 28 Jun 2024
This study investigates the capacity of the VIC model to integrate streamflow and evaporation data in a large-sample application, based on simulations for 189 headwater catchments in Spain. Utilizing multiple datasets to improve model performance is an important aspect of hydrological modeling research, making this paper potential for publication in HESS. However, some important issues are not discussed adequately, and there is room for improvement. Consequently, I recommend a major revision before publication. My major suggestions are as follows:
Given the general imbalances in Q, E, and P, I question whether it is reasonable to use the evaporation dataset to evaluate the model directly. This brings another question: what is the most important signature provided by the evaporation data? Is it the evaporation amount or the temporal variation of evaporation? If the key information provided by the evaporation data is the amount, we can expect that the E simulation obtained by Q-only calibration will be good in the catchments with Q+E/P close to 1, and the NSEQ obtained by Q-E calibration will be lower than that from Q-only calibration (perhaps the authors can test whether the results indeed show this characteristic). Otherwise, if the key information provided by the evaporation data is the variation, conducting a bias correction on E data based on water balance before calibration makes more sense.

An important role of adopting multiple datasets is to reduce equifinality, i.e., to reject some parameters that perform well in the simulation of only one objective. However, the authors didn’t address this issue, stopping at presenting good simulations for streamflow and evaporation. I encourage the authors to discuss the value of the evaporation dataset in reducing equifinality. A potential way to address this issue is to analyze the sensitivity of the integrated objective function to the parameters and compare it with the sensitivity of NSEQ.

Other minor and moderate issues:
-Data:
There are negative records at some stations, which can be attributed to the reservoir. So a question is whether the influence of the reservoir on streamflow is significant and whether the reservoir is simulated in the model. To my knowledge, some rivers significantly influenced by reservoirs have an extremely even interannual streamflow distribution, which is impossible to reproduce if the reservoir is not considered in the model.
-Methods:
The model performance was evaluated using NSE and its decomposition into γ, α, and β. However, to my understanding, γ, α, and β are the decomposition of KGE rather than NSE. NSE actually only quantifies the bias characteristic. I suggest the authors modify this expression. Additionally, please add the equations for these three metrics.
I am a little confused about the SST test. If I understand correctly, this seems to be the common practice in model calibration, i.e., to divide calibration and evaluation periods, with a warm-up period in each. Please correct me if I am wrong, but if this is indeed the common practice, I suggest not referring to it using a special term.
-Sensitivity Analysis:
The authors calculate the sensitivity of model performance to parameters and analyze the correlation between sensitivity and physiographic hydroclimatic characteristics. I think it would be interesting to discuss the mechanism behind this correlation, e.g., why NSEQ is more sensitive to rout1 in catchments with larger precipitation. This can also provide guidance on selecting sensitivity parameters in regions with different conditions. I encourage the authors to delve deeper into this analysis or discussion. Currently, this is only discussed by comparing with other studies, without addressing the underlying reasons.
-The Y-axis of Figure 7 is incorrect.
Citation: https://doi.org/10.5194/hess-2024-57-RC1
- AC1: 'Reply on RC1', Patricio Yeste, 16 Jul 2024
  
  Dear reviewer,
  Our responses can be found in the attached PDF document.
  On behalf of all the authors,
  Patricio Yeste
  
  Citation: https://doi.org/10.5194/hess-2024-57-AC1
RC2:
'Comment on hess-2024-57', Ilhan Özgen-Xian, 04 Jul 2024

Summary. This article investigates how using evapotranspiration in addition to streamflow data during the calibration process affects the model performance across 189 Spanish catchments. The Variable Infiltration Capacity (VIC) model, a semi-distributed hydrological model, is used for modelling. For the calibration of evapotranspiration fluxes, leaf area index and minimal stomatal conductivity were found to be the most sensitive parameters. Apart from the vegetation parameterisation, soil parameters were found to have a large effect on model results. The VIC model is run for 20 years (with a spin-up period of 10 years) using different targets for the calibration. Results are analysed and discussed with a focus on model sensitivity to calibration target.
Assessment. The subject of this article is interesting. Indeed, large sample and large scale hydrological modelling is becoming more feasible, due to improvements in computing technology. In this sense, the topic is timely and of interest. The manuscript is well written and easy to follow. I think the modelling work is substantial and the work is suitable to be published in HESS. I have some questions that I would like the authors to address. These are listed below. I recommend minor revision before publication.
Comment on large sample modelling in this work.
The use of large samples in hydrology is very interesting. Especially when combined with a comparative analysis, the large sample size can help us to discover relations among processes and generate hydrological insights. While this work made use of data from a large number of catchments, the discussion of the results were focused on quite technical issues of model structure and calibration. I do not think this is bad, in fact, these are important topics. But I wonder whether the conclusion in the abstract, i.e. "This investigation will help gain a better understanding of the hydrology of the Spanish catchments and will help prepare the ground for a fully gridded implementation of the VIC model in Spain." still holds. Perhaps in the conclusions, the authors could provide some insights of the hydrology of Spanish catchments they gained in this study. Or perhaps this manuscript focuses on model sensitivity, in this case, this should be better reflected in the abstract.
Questions.
1. Here is my understanding of the modelling work, please correct me if I am wrong: The VIC model is a semi-distributed hydrological model in the sense that no horizontal fluxes are computed between individual grid cells. VIC is set up for entire Spain, thus, all catchments included in this studies data set are represented in the model. Model calibration is done by adjusting model parameters in each grid cell individually.

2. How were soil hydraulic properties aggregated from 1 km to 5 km?

3. Streamflow and evapotranspiration processes have distinct time scales. Are model results of the same variable at different temporal resolution, for example, daily vs. subdaily stream flow, sensitive to different model parameters?

4. Does calibration with only stream flow, only evapotranspiration, and both of them combined result in significantly different model parameterisations?

5. Using the model results in this study, can the relations between the Nash-Sutcliffe efficiencies and model parameters reported in Fig. 6 be interpreted from a hydrological point of view?

6. On page 11, it is reported that a simultaneous calibration with both streamflow and evapotranspiration results in a degradation of model performance. Hydrological models that have been calibrated against more than one type of data often display a greater generalisation capability to changing climate conditions. Can this be seen for the VIC model in the simulated time frame in this study? Is this what is discussed in the last paragraph of Sec. 5.2?

7. Sec. 3.3: What climate forcing was used to spin-up the simulation? From the corresponding 10 years preceeding the simulation period?

Citation: https://doi.org/10.5194/hess-2024-57-RC2
- AC2: 'Reply on RC2', Patricio Yeste, 16 Jul 2024
  
  Dear reviewer,
  Our responses can be found in the attached PDF document.
  On behalf of all the authors,
  Patricio Yeste
  
  Citation: https://doi.org/10.5194/hess-2024-57-AC2
RC3:
'Comment on hess-2024-57', Anonymous Referee #3, 05 Jul 2024

Dear Authors,
thank you very much for your work. I think the work is interesting but I have some concerns:
1- introductioni miss literature review, eg.
a) what about the intercomparision porject MOPEX
b) what about prediction in ungauged catchments
Please, see key references, also for work that has been performend in Spain
Prieto, C., Le Vine, N., Kavetski, D., Fenicia, F., Scheidegger, A., & Vitolo, C. (2022). An exploration of Bayesian identification of dominant hydrological mechanisms in ungauged catchments. Water Resources Research, 58, e2021WR030705. https://doi.org/10.1029/2021WR030705
Prieto, C., Kavetski, D., Le Vine, N., Álvarez, C., & Medina, R. (2021). Identification of dominant hydrological mechanisms using Bayesian inference, multiple statistical hypothesis testing, and flexible models. Water Resources Research, 57, e2020WR028338. https://doi.org/10.1029/2020WR028338
Prieto, C., Le Vine, N., Kavetski, D., García, E., & Medina, R. (2019). Flow prediction in ungauged catchments using probabilistic random forests regionalization and new statistical adequacy tests. Water Resources Research, 55, 4364–4392. https://doi.org/10.1029/2018WR023254

Almeida, S., Le Vine, N., McIntyre, N., Wagener, T., and Buytaert, W.: Accounting for dependencies in regionalized signatures for predictions in ungauged catchments, Hydrol. Earth Syst. Sci., 20, 887–901, https://doi.org/10.5194/hess-20-887-2016, 2016.
Addor, N., Nearing, G., Prieto, C., Newman, A. J., Le Vine, N., & Clark, M. P. (2018). A ranking of hydrological signatures based on their predictability in space. Water Resources Research, 54, 8792–8812. https://doi.org/10.1029/2018WR022606
again, line 31: "accross climates" i suggest you to have a look at Addor et al., 2018
Addor, N., Nearing, G., Prieto, C., Newman, A. J., Le Vine, N., & Clark, M. P. (2018). A ranking of hydrological signatures based on their predictability in space. Water Resources Research, 54, 8792–8812. https://doi.org/10.1029/2018WR022606

Line: 40 "evaluation and benchmarking". i suggest you to have a look at Prieto el al., 2021;2022
Prieto, C., Le Vine, N., Kavetski, D., Fenicia, F., Scheidegger, A., & Vitolo, C. (2022). An exploration of Bayesian identification of dominant hydrological mechanisms in ungauged catchments. Water Resources Research, 58, e2021WR030705. https://doi.org/10.1029/2021WR030705
Prieto, C., Kavetski, D., Le Vine, N., Álvarez, C., & Medina, R. (2021). Identification of dominant hydrological mechanisms using Bayesian inference, multiple statistical hypothesis testing, and flexible models. Water Resources Research, 57, e2020WR028338. https://doi.org/10.1029/2020WR028338
Line 43: parameter regionalization techniques, i recommend u to have a look at
Prieto, C., Le Vine, N., Kavetski, D., García, E., & Medina, R. (2019). Flow prediction in ungauged catchments using probabilistic random forests regionalization and new statistical adequacy tests. Water Resources Research, 55, 4364–4392. https://doi.org/10.1029/2018WR023254

Almeida, S., Le Vine, N., McIntyre, N., Wagener, T., and Buytaert, W.: Accounting for dependencies in regionalized signatures for predictions in ungauged catchments, Hydrol. Earth Syst. Sci., 20, 887–901, https://doi.org/10.5194/hess-20-887-2016, 2016.
line 64: "there is an inreasing tendency towards aridity conditions": what is the difference for different catchments in spain
Study area and data:
line 80: specify the northern districts (there are "many", eg, aguas de galicia, chc, ura, aca)
Also, in section 2 i recomend to provide the range of mean annual precipitation, mean annual flow, mean annual potential evaporanspiration and rainfall runoff coefficient accross catchments and maybe per river basin district in the text . This is to guide the reader.
Line 205: you are using SIMPA as benchmark which we know is a very simple model wrt vic. Maybe, include the pros and cons or similarities and differences as most of the readers won't know what SIMPA is
Discussion:
I miss 1) talking about uncertainty, 2) talking about model structure error, and 3) talking or comparing (which would go to the methods) with a more well stablished model, e.g. gr4j, even if simpa is used as benchmar. So that you would have simpa, vic and gr4j.
I also miss, to compare with other models and results that were run at tdaily time scale in spain, e.g. look at URA
Conclusions:
"The soil and routing parameters were reveled as the most important parameters".
Could you add in which type of catchments were most and least important?

Once again, thank you very much for your work
In case the editor asks for a revised version of the manuscript, i am very happy to serve as reviewer of the revised version
All the Best,
Reviewer

Citation: https://doi.org/10.5194/hess-2024-57-RC3
- AC3: 'Reply on RC3', Patricio Yeste, 16 Jul 2024
  
  Dear reviewer,
  Our responses can be found in the attached PDF document.
  On behalf of all the authors,
  Patricio Yeste
  
  Citation: https://doi.org/10.5194/hess-2024-57-AC3

Patricio Yeste, Matilde García-Valdecasas Ojeda, Sonia R. Gámiz-Fortis, Yolanda Castro-Díez, Axel Bronstert, and María Jesús Esteban-Parra

Data sets

A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments Patricio Yeste, Matilde García-Valdecasas Ojeda, Sonia R. Gámiz-Fortis, Yolanda Castro-Díez, Axel Bronstert, and María Jesús Esteban-Parra https://doi.org/10.5281/zenodo.10670292

Patricio Yeste, Matilde García-Valdecasas Ojeda, Sonia R. Gámiz-Fortis, Yolanda Castro-Díez, Axel Bronstert, and María Jesús Esteban-Parra

Viewed

Total article views: 694 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	BibTeX	EndNote
562	99	33	694	27	27

HTML: 562
PDF: 99
XML: 33
Total: 694
BibTeX: 27
EndNote: 27

Views and downloads (calculated since 08 Mar 2024)

Month	HTML	PDF	XML	Total
Mar 2024	202	39	8	249
Apr 2024	66	6	6	78
May 2024	36	13	3	52
Jun 2024	76	7	5	88
Jul 2024	103	20	11	134
Aug 2024	37	4	0	41
Sep 2024	19	7	0	26
Oct 2024	14	2	0	16
Nov 2024	9	1	0	10

Cumulative views and downloads (calculated since 08 Mar 2024)

Month	HTML	PDF	XML	Total
Mar 2024	202	39	8	249
Apr 2024	66	6	6	78
May 2024	36	13	3	52
Jun 2024	76	7	5	88
Jul 2024	103	20	11	134
Aug 2024	37	4	0	41
Sep 2024	19	7	0	26
Oct 2024	14	2	0	16
Nov 2024	9	1	0	10

Viewed (geographical distribution)

Total article views: 653 (including HTML, PDF, and XML) Thereof 653 with geography defined and 0 with unknown origin.

Country	#	Views	%

Latest update: 20 Nov 2024

Short summary

Integrating streamflow and evaporation data can help improve the physical realism of hydrologic models. In this work we investigate the capabilities of the Variable Infiltration Capacity (VIC) to reproduce both hydrologic variables for 189 headwater located in Spain. Results from sensitivity analysis indicate that adding two vegetation is enough to improve the representation of evaporation, and the performance of VIC exceeded that of the largest modelling effort currently available in Spain.


Total:	0
HTML:	0
PDF:	0
XML:	0