the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Submitted as: model evaluation paper 05 Feb 2021
Submitted as: model evaluation paper | 05 Feb 2021
Modelling Gas Exchange and Biomass Production in West African Sahelian and Sudanian Ecological Zones
- 1Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Garmisch-Partenkirchen, Germany
- 2Laboratoire d’Ecologie Appliquée, Faculté des Sciences Agronomiques, Université d’Abomey-Calavi, Cotonou, Bénin
- 3Biodiversity and Landscape Unit, Université de Liège Gembloux Agro-Bio Tech, Gembloux, Belgium
- 4International Livestock Research Institute (ILRI), Ouagadougou, Burkina Faso
- 5University of Augsburg, Regional Climate and Hydrology Research Group, Augsburg, Germany
- 6International Livestock Research Institute (ILRI), Nairobi, Kenya
- 7HydroSciences Montpellier, Univ. Montpellier, IRD, CNRS, Montpellier, France
- 8Centre de Suivi Ecologique (CSE), Dakar, Senegal
- 9Satellite-based Climate Monitoring, Deutscher Wetterdienst (DWD), Offenbach, Germany
- 10Géosciences Environnement Toulouse (GET), CNRS, IRD, UPS, Toulouse, France
- 11CIRAD, UMR Eco&Sols, BP1386, CP18524, Dakar, Senegal
- 12Eco&Sols, Univ Montpellier, CIRAD, INRAE, IRD, Institut Agro, Montpellier, France
- 13LMI IESOL, Centre IRD-ISRA de Bel Air, BP1386, CP18524, Dakar, Senegal
- 14Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
- 15Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
- 16Department of Physical Geography and Ecosystem Sciences, Lund University, Lund, Sweden
- 1Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Garmisch-Partenkirchen, Germany
- 2Laboratoire d’Ecologie Appliquée, Faculté des Sciences Agronomiques, Université d’Abomey-Calavi, Cotonou, Bénin
- 3Biodiversity and Landscape Unit, Université de Liège Gembloux Agro-Bio Tech, Gembloux, Belgium
- 4International Livestock Research Institute (ILRI), Ouagadougou, Burkina Faso
- 5University of Augsburg, Regional Climate and Hydrology Research Group, Augsburg, Germany
- 6International Livestock Research Institute (ILRI), Nairobi, Kenya
- 7HydroSciences Montpellier, Univ. Montpellier, IRD, CNRS, Montpellier, France
- 8Centre de Suivi Ecologique (CSE), Dakar, Senegal
- 9Satellite-based Climate Monitoring, Deutscher Wetterdienst (DWD), Offenbach, Germany
- 10Géosciences Environnement Toulouse (GET), CNRS, IRD, UPS, Toulouse, France
- 11CIRAD, UMR Eco&Sols, BP1386, CP18524, Dakar, Senegal
- 12Eco&Sols, Univ Montpellier, CIRAD, INRAE, IRD, Institut Agro, Montpellier, France
- 13LMI IESOL, Centre IRD-ISRA de Bel Air, BP1386, CP18524, Dakar, Senegal
- 14Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
- 15Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
- 16Department of Physical Geography and Ecosystem Sciences, Lund University, Lund, Sweden
Abstract. West African Sahelian and Sudanian ecosystems are providing essential services to people and also play a significant role within the global carbon cycle. However, climate and land use are dynamically changing and it remains uncertain how these changes will affect the potential of these regions for providing food and fodder resources or the biosphere-atmosphere exchange of CO2. In this study, we investigate the capacity of a process-based biogeochemical model, LandscapeDNDC, to simulate net ecosystem exchange (NEE) and aboveground biomass of typical managed and natural Sahelian and Sudanian savanna ecosystems. We tested the model for various sites with different proportions of trees and grasses, as well as for the most typical arable cropping systems of the region. In order to describe the phenological development with a common parameterization across all ecosystem types, we introduced soil-water availability in addition to temperature as a driver as seasonal soil water-shortage is a common feature for all these systems. The new approach was tested by using a sample of sites (calibration sites) that provided NEE from flux tower observations and leaf area index data from satellite images (MODIS). For assessing the simulation accuracy, we applied the calibrated model to 42 additional sites (validation sites) across West Africa for which measured aboveground biomass data were available. The model showed a good performance regarding simulated biomass development. Overall, the comparison of simulated and observed biomass at sites with a dominating land cover of crops, grass or trees yielded correlation coefficients of 0.82, 0.94, and 0.77 and the Root Mean Square Error of 0.15, 0.22, and 0.12 kg m−2, respectively. In absolute terms, the model results indicate above-ground carbon stocks up to 1733, 3291, and 5377 kg C ha−1 yr−1 for agricultural, savanna grasslands, and savanna mixed tree-grassland sites. Carbon stocks as well as exchange rates correlated in particular with the abundance of trees. The simulations indicate higher grass biomass and crop yields under more humid climatic conditions as can be found in the Sudanian savanna region. Our study shows the capability of LandscapeDNDC to accurately simulate carbon balances in natural and agricultural ecosystems in semi-arid West Africa under a wide range of conditions, so that it might be used to assess the impact of land-use and climate change on the regional biomass productivity.
Jaber Rahimi et al.
Status: final response (author comments only)
-
CEC1: 'Comment on gmd-2020-417', Juan Antonio Añel, 03 Mar 2021
Dear authors,
Please, in the next step of the review process, be sure that you fix the following issues with your paper:
- The code of the LandscapeDNDC model must be stored in a permanent archive. For example, Zenodo.
- You must include the name of the model and the version that you use in the title of the manuscript.Best regards,
Juan A. Añel
Geosc. Mod. Dev. Executive Editor-
AC1: 'Reply on CEC1', Rüdiger Grote, 05 Mar 2021
Thank you for indicating the issues that we will fix with the next step of the reviewing process. More specifically,
- we will change the title, probably by adding 'using LandscapeDNDC' (also depends on potential requests of the reviewers that might lead to further changes but that will in any case include the model name)
- we will provide the executable as well as the model code of the respective model version within a permanent archive that will be hosted by the KIT/IMK-IFU and will be accessible freely via the models website.
Best regards
-
CEC2: 'Reply on AC1', Juan Antonio Añel, 05 Mar 2021
Dear authors,
Please, be sure that the chosen archive complies with our 'Archive standards'. We could request evidence that it is funded to be available and online for over more than a decade.
Best regards,
Juan A. Añel
Geosc. Mod. Dev. Executive Editor
-
AC2: 'Reply on CEC2', Rüdiger Grote, 05 Mar 2021
Thanks for the reminder. We have considered the archive standards and can confirm that we are able to provide sufficient support for data security, identification, and availability (possibly with the support of the KIT data management support, https://www.scc.kit.edu/en/services/5591.php). We will specify the exact means in further elaborations of the manuscript.
Best regards
-
AC2: 'Reply on CEC2', Rüdiger Grote, 05 Mar 2021
-
CEC2: 'Reply on AC1', Juan Antonio Añel, 05 Mar 2021
-
AC1: 'Reply on CEC1', Rüdiger Grote, 05 Mar 2021
-
RC1: 'Comment on gmd-2020-417', Zhipin Ai, 11 Mar 2021
The comment was uploaded in the form of a supplement: https://gmd.copernicus.org/preprints/gmd-2020-417/gmd-2020-417-RC1-supplement.pdf
-
AC3: 'Reply on RC1', Rüdiger Grote, 23 Apr 2021
General remarks
We are grateful for the remarks and suggestion that we happily used to improve the manuscript as described in detail below. In particular, we added a new figure that evaluates satellite derived LAI information (Fig.2), complemented Figure 1 and Table 1 with additional site information, improved the model description including new parameter indications, and added information into Figures 3, 5, and 7 for a better evaluate the representation of water balance simulations. Along the revision, a couple of minor corrections have been made regarding language or logical problems. As far as these led to additions to the text, they are all indicated in track-change mode as are the changes that had been done in response to the specific remarks.
General remarks (both reviewers)
We are grateful for the remarks and suggestion that we happily used to improve the manuscript as described in detail below. In particular, we added a new figure that evaluates satellite derived LAI information (Fig.2), complemented Figure 1 and Table 1 with additional site information, improved the model description including new parameter indications, and added information into Figures 3, 5, and 7 for a better evaluate the representation of water balance simulations. Along the revision, a couple of minor corrections have been made regarding language or logical problems. As far as these led to additions to the text, they are all indicated in track-change mode as are the changes that had been done in response to the specific remarks.
Specific answers to the remarks of reviewer 1 (R: Repeated remark of the reviewer; A: Answer, concrete changes in the text as indicated with bold letters)
R1: The authors underscored the importance of the soil water availability to this special region and ecosystem, they considered 70% of the extractable soil water content, I am wondering whether it is vegetation specific since there are three distinct types here (croplands, pure grasslands as well as tree/grass mixtures)? I suggest adding a sensitivity test to illustrate its influence. Or, just set different thresholds for different vegetation (this might be doable since the simulation here was conducted at site level instead of regional level).
A1: The extractable soil water has in fact been set from field measurements per site as the difference between field capacity and soil wilting point, stratified in different layers. The 70% of extractable water has been set as the threshold value after which photosynthesis is linear reduced until it is zero when the wilting point is reached. For consistency reasons we thus added the respective parameter H2OREF_A into Table 3 and refer to it in the text. However, we found in the FAO database that 70% is considered rather at the upper end of the range and a value of 50% (45% for C4 plants) is recommended. Therefore, we now use these values and have recalculated the simulations accordingly. Since the literature information for this threshold value is still rather scarce, we have additionally checked the impact of the threshold parameter by varying it from 30 to 70%. The sensitivity analysis showed that the change in biomass production was between 0 and 11 percent depending on sites and crops. By using 50% for all investigated crops the results vary by +/- 7 percent at a maximum. This indicates that the parameter is not overly sensitive and that the selected values are a reasonable choice.
For applying the model at less known sites (not in this investigation), we regard the total extractable water as directly related to the soil type (e.g. Sand, Clay, Loam, etc.) and thus only indirectly to vegetation type (since agricultural vegetation is generally grown on more fertile soils).
R2: For me, the LAI at Niakhar was underestimated, and that at Wankama 1 was overestimated, NEE at Bontioli was overestimated, is it possible to have more discussion about the reason or adjust the parameters to have a better capture?
A2: Thank you for the remark. Indeed, the local measurements of LAI in Niakhar indicate a slightly higher LAI than resulted from the model while the remote sensing misses the peaks that we represent in the Savanna region of Wankama 1. Regarding Niakhar, trees in the area that are not considered for the simulation of agricultural site likely lead to a higher LAI than simulated. In Wankama, this is likely related to the widespread occurrence of bushes in the region that were not considered in the modelled grassland type, leading the NDVI and thus LAI estimates to be larger outside the season but providing competition to the grass which prevents its full biomass development. We have now included these discussion points into the text supplying also literature references that support these arguments (L360ff, L431ff).
R3: There is a long description about the model in 2.3.1, I suggest adding the important equations to make it easy to understand the revenant biophysical process of the model since not all the readers are familiar with the model. For example, how is the actual evaporation calculated from the potential evaporation? Also, the Thornthwaite approach mainly depends on temperature, it seems water content is important in this special region with large variation of precipitation, so, how is the impact on the result? How is the performance of the modelled evaporation compared to the flux observation?
A3: Since LDNDC is a rather complex model, it is difficult to supply a few equations that are most important. In particular if the description should not be extended. However, we have gone through the description in order to clarify potential difficulties. In particular, we have now better explained the Thornthwaite approach, which indeed is based on temperature (and daylength period), and added the description of how potential evapotranspiration is used to derive potential transpiration demand as well as the various evaporation fluxes (L255ff).
The performance of the model to represent water fluxes above grassland and groundnut has been evaluated at the Bontioli site in a previous publication (Grote et al. 2009b). Regarding the other sites, we compared evaporation data from graphs in various publications of the sites Agoufou 2006-7, Wankama 2005-6, and Sumbrungu/Nazinga/Kayoro 2013 which were visually in good accordance to the simulated fluxes. In order to demonstrate that the water balance is reasonably well represented, we have introduced the measured soil water conditions at each site along with the simulated ones (new Fig. 3, 5, and 7). The comparison of both shows that the dynamics are very well met, with minor deviations at Nalohou and Agoufou only, where the water storage capacity has been underestimated in both cases. Results are additionally discussed (L366ff, L412ff).
R4: How is the parameters relevant to soil water content (field capacity and wilting point) at each site? Please clarify.
A4: In order to save space, we added the water holding capacity (total field capacity – wilting point in mm m-3) for each site into Table 1.
R5: I suggest adding the vegetation distribution in Fig. 1, which is more intuitive and easier to understand.
A5: Thanks for the recommendation. We added the vegetation type distribution into Fig. 1 accordingly.
R6: The authors used the Modis LAI, which is 500-m resolution, if the grid is a mixture of different vegetation, it might have a big impact on the validation. Such discussions are needed.
A6: We agree that this issue is worth investigating in more detail. Therefore, we have added a comparison of MODIS derived and site-specific LAI measurements (new Fig. 2) and discuss the deviations between both (L205ff). Indeed, the resolution of MODIS data is an important point here, which is further corroborated by new literature cited.
R7: Line 318-320, how is the standard for spinup? It says it accounts for the competition on light and water at the sites. Please clarify this in detail.
A7: The standard spin-up procedure has been previously explained in the same chapter with regard to agricultural sites. It consists of a three-year simulation run that is primarily needed to account for uncertain initializations of soil carbon and nitrogen pools. In case of grasslands, where the biomass initialization is also highly uncertain and varies from year to year, this spin-up period also helps to avoid steep adjustments due to specific site conditions during the first years. Since we recognized that this description should not be placed into different paragraphs, we now join the text passages and put it in front of chapter 2.3.2 (Model setup, L278ff).
R8: Minor, I think gas exchange might be removed from the title since it is not reflected in the main body.
A8: We beg your pardon but after some consideration we think that the relatively extensive NEE modelling part justifies mentioning gas exchange in the title.
R9: Minor, I fell the abstract is quite long and should be more concise.
A9: Thank you for the recommendation. We have gone through the abstract and made it more stringent.
R10: Minor, superscript in the figure is ignored, please modify.
A10: Thank you for noticing. We corrected the letter style in the Figures (now 4, 6, 8).
-
AC3: 'Reply on RC1', Rüdiger Grote, 23 Apr 2021
- RC2: 'Comment on gmd-2020-417', Anonymous Referee #2, 13 Mar 2021
Jaber Rahimi et al.
Jaber Rahimi et al.
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 383 | 138 | 21 | 542 | 5 | 9 |
- HTML: 383
- PDF: 138
- XML: 21
- Total: 542
- BibTeX: 5
- EndNote: 9
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1