the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 22 May 2023
Representing inter-annual land cover and vegetation variability based on satellite observations in the HTESSEL land surface model
Fransje van Oorschot
Ruud J. van der Ent
Markus Hrachowitz
Emanuele Di Carlo
Franco Catalano
Souhail Boussetta
Gianpaolo Balsamo
Andrea Alessandri
Abstract. Vegetation largely controls land surface-atmosphere interactions. Although vegetation is highly dynamic across spatial and temporal scales, most land surface models currently used for reanalyses and near-term climate predictions do not adequately represent these dynamics. This causes deficiencies in the variability of modeled water and energy states and fluxes from the land surface. In this study we evaluated the effects of integrating spatially and temporally varying land cover and vegetation characteristics derived 5 from satellite observations on modelled evaporation and soil moisture in the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) land surface model. Specifically, we integrated inter-annually varying land cover from the European Space Agency Climate Change Initiative, and inter-annually varying Leaf Area Index (LAI) from the Copernicus Global Land Services (CGLS). Additionally, satellite data of the Fraction of green vegetation Cover (FCover) from CGLS was used to formulate and integrate a spatially and temporally varying effective vegetation cover parameterization. The effects of these three implementations on model evaporation fluxes and soil moisture were analysed using historical offline (land-only) model experiments at the global scale, and model performances were quantified with global observational products of evaporation (E) and near-surface soil moisture (SMs). The inter-annually varying land cover consistently altered the evaporation and soil moisture in regions with major land-cover changes. The inter-annually varying LAI considerably improved the correlation of SMs and E with respect to the reference data, with largest improvements in semiarid regions with predominantly low vegetation during the dry season. These improvements are related to the activation of soil moisture-evaporation feedbacks during vegetation-water-stressed periods with inter-annually varying LAI in combination with inter-annually varying effective vegetation cover, defined as an exponential function of LAI. The further improved effective vegetation cover parameterization consistently reduced the errors of model effective vegetation cover, and it regionally improved SMs and E. Overall, our study demonstrated that the enhanced vegetation variability consistently improved the near-surface soil moisture and evaporation variability, but the availability of reliable global observational data remains a limitation for complete understanding of the model response. To further explain the improvements found, we developed an interpretation framework for how the model development activates feedbacks between soil moisture, vegetation, and evaporation during vegetation-water-stress periods.
-
Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
-
Preprint
(6586 KB)
-
Supplement
(3654 KB)
-
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(6586 KB) - Metadata XML
-
Supplement
(3654 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Fransje van Oorschot et al.
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-803', Anonymous Referee #1, 08 Jun 2023
Please see attached PDF for reviewer comments.
- AC1: 'Reply on RC1', Fransje van Oorschot, 01 Sep 2023
-
RC2: 'Comment on egusphere-2023-803', Anonymous Referee #2, 13 Jul 2023
This paper investigates the effects of integrating inter-annually varying land cover and vegetation characteristics, derived from satellite observations, on modeled evaporation and soil moisture. The study uses the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) Land Surface Model (LSM) and data from various sources, including ESA-CCI land cover maps, Copernicus Global Land Services (CGLS) data on Leaf Area Index (LAI), and Fraction of green vegetation Cover (FCover) from CGLS. The paper concludes that integrating interannually varying land cover and vegetation significantly improves the representation of evaporation and soil moisture, highlighting the importance of capturing vegetation variability for accurate modeling of land surface-atmosphere interactions. The findings have important implications for refining land surface models and improving the accuracy of climate change predictions. Overall, this paper is well-written and scientifically sound. It provides valuable insights into the future of LSM modeling in the context of pervasive global change. However, several concerns need to be adequately addressed prior to publication.
The title of the paper does not effectively summarize the main research content and findings. While representing inter-annual land cover and vegetation variability based on remote sensing data is a part of the paper, another part involves studying the improvement of model simulation ability for E and SM after introducing remote sensing data. Therefore, I recommend that the author modify the title accordingly.
The Introduction section could be further improved. The importance of the research in this paper was not well explained. For example, Line 58: The authors may need to justify the statement “most previous LSM studies aimed at improving the temporally fixed boundary condition of land cover and the monthly seasonal cycle of LAI, while not exploring the effects of inter-annual variations of LC and LAI”. Numerous studies have attempted to simulate Leaf Area Index (LAI) using Land Surface Models (LSM). These models can also consider the dynamics of vegetation cover, although uncertainties cannot be ignored. Also, the statement "Moreover, previous studies have generally used one spatially fixed relationship between effective vegetation cover and LAI, ..." requires further explanation and justification.
Line 77: Why didn't the authors use the AVHRR LAI data directly instead of using a combined dataset from AVHRR and CGLS LAI?
Line 84: Could the authors please provide a brief explanation of the improvements made in this study compared to the previous one? This would help to better understand the novelty of the current study.
Line 100: Why 289 cm? Should it be 189 cm?
Section 2.2.2 mainly describes how LAI affects RC and W1m, rather than the representation of LAI itself. The same issue applies to Sections 2.2.1 and 2.2.3.
Line 116: Did the authors take the effects of rising CO2 on rc into account?
Line 185: The spatial resolution here is approximately 75x75 km. Why was the LAI and LC data interpolated to 1x1 km?
Section 4.1: The section title does not fit in the discussion section. Additionally, I noticed that the text below does not only summarize the results.
Line 389: It is misleading to say "fixed atmospheric forcing" here.
Section 4.2: While it is commendable to acknowledge these limitations, I suggest that the authors provide a more detailed discussion. For example, they could explore the differences in the representation of land cover and vegetation variability between the models used in this study and the ERA5 LSM, and discuss the potential consequences for the comparative analysis.
Line 435: Many efforts have been made to model global vegetation dynamics, although notable uncertainties still exist. Furthermore, the term ‘vegetation evolution’ may not be appropriate.
Citation: https://doi.org/10.5194/egusphere-2023-803-RC2 - AC2: 'Reply on RC2', Fransje van Oorschot, 01 Sep 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-803', Anonymous Referee #1, 08 Jun 2023
Please see attached PDF for reviewer comments.
- AC1: 'Reply on RC1', Fransje van Oorschot, 01 Sep 2023
-
RC2: 'Comment on egusphere-2023-803', Anonymous Referee #2, 13 Jul 2023
This paper investigates the effects of integrating inter-annually varying land cover and vegetation characteristics, derived from satellite observations, on modeled evaporation and soil moisture. The study uses the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) Land Surface Model (LSM) and data from various sources, including ESA-CCI land cover maps, Copernicus Global Land Services (CGLS) data on Leaf Area Index (LAI), and Fraction of green vegetation Cover (FCover) from CGLS. The paper concludes that integrating interannually varying land cover and vegetation significantly improves the representation of evaporation and soil moisture, highlighting the importance of capturing vegetation variability for accurate modeling of land surface-atmosphere interactions. The findings have important implications for refining land surface models and improving the accuracy of climate change predictions. Overall, this paper is well-written and scientifically sound. It provides valuable insights into the future of LSM modeling in the context of pervasive global change. However, several concerns need to be adequately addressed prior to publication.
The title of the paper does not effectively summarize the main research content and findings. While representing inter-annual land cover and vegetation variability based on remote sensing data is a part of the paper, another part involves studying the improvement of model simulation ability for E and SM after introducing remote sensing data. Therefore, I recommend that the author modify the title accordingly.
The Introduction section could be further improved. The importance of the research in this paper was not well explained. For example, Line 58: The authors may need to justify the statement “most previous LSM studies aimed at improving the temporally fixed boundary condition of land cover and the monthly seasonal cycle of LAI, while not exploring the effects of inter-annual variations of LC and LAI”. Numerous studies have attempted to simulate Leaf Area Index (LAI) using Land Surface Models (LSM). These models can also consider the dynamics of vegetation cover, although uncertainties cannot be ignored. Also, the statement "Moreover, previous studies have generally used one spatially fixed relationship between effective vegetation cover and LAI, ..." requires further explanation and justification.
Line 77: Why didn't the authors use the AVHRR LAI data directly instead of using a combined dataset from AVHRR and CGLS LAI?
Line 84: Could the authors please provide a brief explanation of the improvements made in this study compared to the previous one? This would help to better understand the novelty of the current study.
Line 100: Why 289 cm? Should it be 189 cm?
Section 2.2.2 mainly describes how LAI affects RC and W1m, rather than the representation of LAI itself. The same issue applies to Sections 2.2.1 and 2.2.3.
Line 116: Did the authors take the effects of rising CO2 on rc into account?
Line 185: The spatial resolution here is approximately 75x75 km. Why was the LAI and LC data interpolated to 1x1 km?
Section 4.1: The section title does not fit in the discussion section. Additionally, I noticed that the text below does not only summarize the results.
Line 389: It is misleading to say "fixed atmospheric forcing" here.
Section 4.2: While it is commendable to acknowledge these limitations, I suggest that the authors provide a more detailed discussion. For example, they could explore the differences in the representation of land cover and vegetation variability between the models used in this study and the ERA5 LSM, and discuss the potential consequences for the comparative analysis.
Line 435: Many efforts have been made to model global vegetation dynamics, although notable uncertainties still exist. Furthermore, the term ‘vegetation evolution’ may not be appropriate.
Citation: https://doi.org/10.5194/egusphere-2023-803-RC2 - AC2: 'Reply on RC2', Fransje van Oorschot, 01 Sep 2023
Peer review completion
Journal article(s) based on this preprint
Fransje van Oorschot et al.
Fransje van Oorschot et al.
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 355 | 125 | 27 | 507 | 77 | 15 | 17 |
- HTML: 355
- PDF: 125
- XML: 27
- Total: 507
- Supplement: 77
- BibTeX: 15
- EndNote: 17
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(6586 KB) - Metadata XML
-
Supplement
(3654 KB) - BibTeX
- EndNote
- Final revised paper