the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Spatial heterogeneity effects on land surface modeling of water and energy partitioning
- Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
- Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
Abstract. Understanding the influence of land surface heterogeneity on surface water and energy fluxes is crucial for modeling earth system variability and change. This study investigates the effects of four dominant heterogeneity sources on land surface modeling, including atmospheric forcing (ATM), soil properties (SOIL), land use and land cover (LULC), and topography (TOPO). Our analysis focused on their impacts on the partitioning of precipitation (P) into evapotranspiration (ET) and runoff (R), partitioning of net radiation into sensible heat and latent heat, and corresponding water and energy fluxes. A set of 16 experiments were performed over the continental U.S. (CONUS) using the E3SM land model (ELMv1) with different combinations of heterogeneous and homogeneous datasets. The Sobol' total sensitivity analysis is utilized to quantify the relative importance of the four heterogeneity sources. Results show that ATM and LULC are the most dominant heterogeneity sources in determining spatial variability of water and energy partitioning, and their heterogeneity effects are complementary both spatially and temporally. The overall impacts of SOIL and TOPO are negligible, except TOPO dominates the spatial variability of R/P across the transitional climate zone between the arid western and humid eastern CONUS. Comparison with ERA5-Land reanalysis reveals that accounting for more heterogeneity sources improves the simulated spatial variability of water and energy fluxes. An additional set of 13 experiments identified the most critical components within the heterogeneity sources: precipitation, temperature and longwave radiation for ATM, soil texture and soil color for SOIL, and maximum fractional saturated area parameter for TOPO.
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Lingcheng Li et al.
Status: final response (author comments only)
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RC1: 'Comment on gmd-2022-4', Hui Zheng, 23 Mar 2022
This study investigated the effects of spatial heterogeneity in atmospheric forcing, land use and land cover, soil properties, and topography on the modeling of evapotranspiration, runoff, and their components (i.e., canopy evaporation, ground evaporation, transpiration, surface and subsurface runoff). The design of the numerical experiments is reasonable, the methods are innovative, and the results are insightful. With the above considerations, I suggests a publication with several clarifications.
Detailed comments
- L121--L124: Better put them in the Methodology section.
- L135, 2013a Li et al., 2013: typos?
- L136--L140: What is the purpose of these statements? They duplicate the discussion. On the other hand, more descriptions of ELM on the used parameterizations are desirable.
- L166--L167: How did you resample soil properties? How soil texture and organic matter are upscaled, and how soil color is downscaled?
- L199--L209: The calculation of Sobol's sensitivity index is still a bit confused to me. Assuming that X is the 30-year monthly value, SENSITIVITY(X) is the Sobol's sensitivity index of X, and 30-YEAR-SEASONAL-AVERAGE(X) is the seasonal average of X, did you calculate the index as SENSITIVITY(30-year-seasonal-average(X)) or 30-year-seasonal-average(SENSITIVITY(X))?
- Table 4: Did you first propose this approach?
- L260: It would be to compare the spatial variability with the temporal variability or the mean value of ET/P.
- L305--L328 and Figure 5: I did not get the objective of these contents. Since ATM and LULC are dominant, it is quite natural that their contributions to the spatial variability are complementary. Deletion seems fine and would make the paper concise.
- L392-L395: Do the interplay between the spatial variabilities in ATM, LULC, SOIL, and TOPO increase or decrease the overall variability?
- Section 3.5 and Figure 8: It is interesting to see the comparison between ERA5-Land and the 0.125-degree ELM simulations. Since the ERA5-Land atmospheric forcing is interpolated from the ~31-km ERA5 data with the consideration of elevation dependency (doi:10.5194/essd-13-4349-2021) rather than upscaled from the finer-scale observations, the spatial variability from ERA5-Land would be smaller than that from ELM. In Figure 8, it would be better to show the difference rather than absolute difference to check this. -
RC2: 'Comment on gmd-2022-4', Anonymous Referee #2, 19 Apr 2022
This manuscript examines the relative importance of four heterogeneity sources on the spatially aggregated variability of some modeled components of the water and energy partitioning over CONUS. The study is appropriately motivated and, overall, well written. The presented methods are adequate and innovative, and the results reasonable. I suggest publication after some minor revisions and clarifications.
L136 – L140: Since this study did not use the described model developments, I recommend removing this paragraph. Instead, details on the methods used to characterize the land surface heterogeneity within ELM for this study would be appreciated.
L166 – L167: Please clarify the methods used to resample the original datasets to 0.125°.
L251 – L252: Please clarify the methods used to resample.
Section 2.4: Have you also considered analyzing the first-order Sobol sensitivity index? The first-order index would measure just the direct effect of each heterogeneity source on the variance of the model. Compared to the total sensitivity, the first order index would explain the importance of the interactions between heterogeneity sources (e.g., topography and atmospheric forcing; soils and topography).
L361 – L370: A more comprehensive analysis of the factors explaining the seasonal variation of the importance of heterogeneity sources would be appreciated here. For instance, what drives the increased relevance of topography in zones of the East in Summer and Fall.
Table 5: I recommend moving this table to Supplementary material and including Figure S5 in the main text (primarily, since L361 to L370 mainly focus on analyzing maps).
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CEC1: 'Comment on gmd-2022-4', Juan Antonio Añel, 25 Apr 2022
Dear authors,
After checking your manuscript, it has come to our attention that it does not comply with our Code and Data Policy.
https://www.geoscientific-model-development.net/policies/code_and_data_policy.html
You have archived your code on GitHub. However, GitHub is not a suitable repository. GitHub itself instructs authors to use other alternatives for long-term archival and publishing, such as Zenodo (GitHub provides a direct way to copy your project to a Zenodo repository). Therefore, please, publish your code in one of the appropriate repositories according to our policy.
In this way, you must include in a potential reviewed version of your manuscript the modified 'Code and Data Availability' section, including the DOI of the code.
Also, the repositories you provide for data (NCAR and NASA servers) do not comply with our policy. I understand that you could not be the owner of the data, and therefore you could not have the right to copy them to one of the repositories acceptable. However, I would ask you to contact the responsible for such repositories and ask them to store a copy in a suitable one or that they permit you to do it. By the way, the hyperlinks to the repositories included in the manuscript are wrong. You have included at the end of both an extra ";"; therefore, when somebody tries to open them, they do not work.
Please, reply as soon as possible to this comment with the link and DOI for the new repositories so that they are available for the peer-review process, as they should be.Juan A. Añel
Geosci. Model Dev. Executive Editor
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AC1: 'Reply on CEC1', Lingcheng Li, 26 Apr 2022
Dear Editor,
Thanks for reminding us regarding the Code and Data Policy. First, we updated the data links with “;” issue, and these links should be right now. We also archived our model and data into the Zendo (https://doi.org/10.5281/zenodo.6484857). The Code and Data Availability will be updated in the modified manuscript.
Code and Data Availability. The source code of ELMv1 is available from https://github.com/E3SM-Project/E3SM (last access: September 2020); NLDAS-2 forcing is available from https://ldas.gsfc.nasa.gov/nldas/v2/forcing. SOIL and TOPO related datasets are downloaded from https://svn-ccsm-inputdata.cgd.ucar.edu/trunk/inputdata/lnd/clm2/rawdata/. LULC related datasets are from Ke et al. (2012); ERA5-Land reanalysis is available from: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=overview. The exact version of ELM source code and surface data (e.g., SOIL, TOPO, LULC) used in this study are archived on Zenodo (https://doi.org/10.5281/zenodo.6484857)
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CEC2: 'Reply on AC1', Juan Antonio Añel, 26 Apr 2022
Dear authors,
Many thanks for making the code available on Zenodo.org. Please, remove from the statement the references to GitHub. The Code and Data Availability section is not intended to advertise, promote or point out where to find the last version of your model, but to assure replicability of the work submitted. In this way, the only relevant version is the one stored in Zenodo.org, and any other link is irrelevant and can introduce unnecessary confusion.
Juan A. Añel
Geosci. Model Dev. Executive Editor
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CEC2: 'Reply on AC1', Juan Antonio Añel, 26 Apr 2022
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AC1: 'Reply on CEC1', Lingcheng Li, 26 Apr 2022
Lingcheng Li et al.
Lingcheng Li et al.
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