Future vegetation projection by Earth system model and its impact on nomadism
This paper reports the progress of our efforts to apply the livestock weight model developed by ourselves in the past to future scenarios. First, I analyzed the results of future scenario experiments of global climate models that incorporate biogeochemical cycles including ecosystems (e.g., Earth sy...
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| Published in | Journal of Arid Land Studies Vol. 33; no. 1; pp. 43 - 49 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Japanese |
| Published |
The Japanese Association for Arid Land Studies
30.06.2023
日本沙漠学会 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0917-6985 2189-1761 |
| DOI | 10.14976/jals.33.1_43 |
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| Abstract | This paper reports the progress of our efforts to apply the livestock weight model developed by ourselves in the past to future scenarios. First, I analyzed the results of future scenario experiments of global climate models that incorporate biogeochemical cycles including ecosystems (e.g., Earth system model), and confirmed that the amount of vegetation around Mongolia will basically be projected to increase, and that the degree of this increase is more emphasized in the high temperature scenarios. This can be attributed to the effects of carbon dioxide fertilization, higher temperatures, and higher nitrogen concentrations by fertilizers etc. Next, to prepare input data for the livestock weight model, I presented the results of attempting downscaling method combining an offline vegetation model and a convolutional neural network. The former method achieved a spatial resolution of 0.5°×0.5°, which could be further refined to 8 km×8 km by the latter method. Although some technical problems remain, the results showed the possibility of obtaining future vegetation distribution with sufficient spatial resolution for analyzing nomadism. Finally, using the Leaf Area Index (LAI) distribution for 2021-2030 with RCP 8.5 scenario derived by the above method, and given the assumption that livestock move to the grid with the largest LAI value in the surrounding area each month, the LAI of the grid where livestock stay was calculated. Here, the average LAI for the year preceding the month of April, when livestock weight drops, was presented. In the future, I intend to further improve the downscaling method and the livestock weight model. |
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| AbstractList | This paper reports the progress of our efforts to apply the livestock weight model developed by ourselves in the past to future scenarios. First, I analyzed the results of future scenario experiments of global climate models that incorporate biogeochemical cycles including ecosystems (e.g., Earth system model), and confirmed that the amount of vegetation around Mongolia will basically be projected to increase, and that the degree of this increase is more emphasized in the high temperature scenarios. This can be attributed to the effects of carbon dioxide fertilization, higher temperatures, and higher nitrogen concentrations by fertilizers etc. Next, to prepare input data for the livestock weight model, I presented the results of attempting downscaling method combining an offline vegetation model and a convolutional neural network. The former method achieved a spatial resolution of 0.5°×0.5°, which could be further refined to 8 km×8 km by the latter method. Although some technical problems remain, the results showed the possibility of obtaining future vegetation distribution with sufficient spatial resolution for analyzing nomadism. Finally, using the Leaf Area Index (LAI) distribution for 2021-2030 with RCP 8.5 scenario derived by the above method, and given the assumption that livestock move to the grid with the largest LAI value in the surrounding area each month, the LAI of the grid where livestock stay was calculated. Here, the average LAI for the year preceding the month of April, when livestock weight drops, was presented. In the future, I intend to further improve the downscaling method and the livestock weight model. This paper reports the progress of our efforts to apply the livestock weight model developed by ourselves in the past to future scenarios. First, I analyzed the results of future scenario experiments of global climate models that incorporate biogeochemical cycles including ecosystems (e.g., Earth system model), and confirmed that the amount of vegetation around Mongolia will basically be projected to increase, and that the degree of this increase is more emphasized in the high temperature scenarios. This can be attributed to the effects of carbon dioxide fertilization, higher temperatures, and higher nitrogen concentrations by fertilizers etc. Next, to prepare input data for the livestock weight model, I presented the results of attempting downscaling method combining an offline vegetation model and a convolutional neural network. The former method achieved a spatial resolution of 0.5°×0.5°, which could be further refined to 8 km×8 km by the latter method. Although some technical problems remain, the results showed the possibility of obtaining future vegetation distribution with sufficient spatial resolution for analyzing nomadism. Finally, using the Leaf Area Index (LAI) distribution for 2021-2030 with RCP 8.5 scenario derived by the above method, and given the assumption that livestock move to the grid with the largest LAI value in the surrounding area each month, the LAI of the grid where livestock stay was calculated. Here, the average LAI for the year preceding the month of April, when livestock weight drops, was presented. In the future, I intend to further improve the downscaling method and the livestock weight model. 過去に自ら開発した家畜体重モデルの将来シナリオへの適用を目的とし，現在取り組んでいることの進捗を報告する．まず，全球気候モデルのうち，生態系などの物質循環を含むモデル（地球システムモデル）の将来シナリオ実験結果を解析し，モンゴル周辺では将来基本的に植生量が増えると予測されていること，その度合いは高温シナリオでより顕著なことを確認した．これは，二酸化炭素の施肥効果，気温上昇，施肥などによる窒素濃度上昇が原因と考えられる．次に家畜体重モデルへの入力データの準備のため，オフライン植生モデルと畳み込みニューラルネットワークとを組み合わせたダウンスケーリング手法の試行結果を示した．前者により0.5°×0.5°の解像度が実現され，さらに後者により8 km×8 kmまで細かくできた．これにより，いくつかの技術的問題が残されているものの，遊牧の解析を行う上で十分な解像度で将来の植生量分布が得られる可能性を示した．最後に，上記手法で作成した2021-2030年のRCP8.5シナリオについてのLAI（葉面積指数）分布を用い，毎月家畜が周囲で最もLAI値が高いグリッドに移動するという仮定を与え，家畜が滞在するグリッドのLAIを計算し，家畜体重が低下する4月を基準に直前の1年間の平均LAIを示した．今後は，ダウンスケール手法および家畜体重モデルの改良をさらに進めたいと考えている． |
| Author | TACHIIRI, Kaoru |
| Author_FL | 立入 郁 |
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| References | Komiyama H. (2013): Looking back on the 2010 dzud (cold and snow disaster) in Mongolia. Nihon to Mongol (Japan and Mongolia), 126: 33-38. O’Neill B. C., Tebaldi C., van Vuuren D. P., Eyring V., Friedlingstein P., Hurtt G., Knutti R., Kriegler E., Lamarque J. -F., Lowe J., Meehl G. A., Moss R., Riahi K., Sanderson B. M. (2016): The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9: 3461-3482. Dong C., Loy C. C., He K., Tang X. (2014): Image Super-Resolution using Deep Convolutional Networks. https://doi.org/10.48550/arXiv.1501.00092 Abadi M., Agarwal A., Barham P., Brevdo E., Chen Z., Citro C., Corrado G. S., Davis A., Dean J., Devin M., Ghemawat S., Goodfellow I. J., Harp A., Irving G., Isard M., Jia Y., Józefowicz R., Kaiser L., Kudlur M., Levenberg J., Mané D., Monga R., Moore S., Murray D. G., Olah C., Schuster M., Shlens J., Steiner B., Sutskever I., Talwar K., Tucker P. A., Vanhoucke V., Vasudevan V., Viégas F. B., Vinyals O., Warden P., Wattenberg M., Wicke M., Yu Y., Zheng X. (2015): TensorFlow: Large-scale machine learning on heterogeneous systems, Software available from tensorflow.org. Komiyama H. (2005): Actual damage to Mongolian animal husbandry due to the 2000-2002 dzud disaster. Bull. Jpn. Assoc. Mongolian Studies, 35: 73-85. Sakamoto T. (2016): Computational research on mobile pastoralism using agent-based modeling and satellite imagery. PLoS ONE, 11: e0151157. Ito A., Oikawa T. (2002): A simulation model of the carbon cycle in land ecosystems (Sim-CYCLE): a description based on dry-matter production theory and plot-scale validation. Ecol. Model., 151: 147-179. Begzsuren S., Ellis J. E., Ojima D. S., Coughenour M. B., Chuluun T. (2004): Livestock responses to drought and severe winter weather in the Gobi Three Beauty National Park, Mongolia. J. Arid Environ., 59: 785-796. Moritz M., Hamilton I. M., Yoak A. J., Scholte, P., Cronley J., Maddock, P., Pi H. Y. (2015): Simple movement rules result in ideal free distribution of mobile pastoralists. Ecol. Modell., 305: 54-63. Tachiiri K., Komiyama H., Morinaga Y., Shinoda M. (2017): Application of a livestock weight model to the 2009-2010 winter disaster in Mongolia. Rangel. J., 39: 263-277. Hajima T., Watanabe M., Yamamoto A., Tatebe H., Noguchi M. A., Abe M., Ohgaito R., Ito A., Yamazaki D., Okajima H., Ito A., Takata K., Ogochi K., Watanabe S., Kawamiya M. (2020): Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks. Geosci. Model Dev., 13: 2197-2244. Tachiiri K., Shinoda M. (2012): Quantitative risk assessment for future meteorological disasters: reduced livestock mortality in Mongolia. Clim. Change, 113: 867-882. Tachiiri K., Shinoda M., Klinkenberg B., Morinaga Y. (2008): Assessing Mongolian snow disaster risk using livestock and satellite data. J. Arid Environ., 72: 2251-2263. Sternberg T. (2010): Unravelling Mongolia’s extreme winter disaster of 2010. Nomadic Peoples, 14: 72-86. Tebaldi C., Debeire K., Eyring V., Fischer E., Fyfe J., Friedlingstein P., Knutti R., Lowe J., O’Neill B., Sanderson B., van Vuuren D., Riahi K., Meinshausen M., Nicholls Z., Tokarska K. B., Hurtt G., Kriegler E., Lamarque J. -F., Meehl G., Moss R., Bauer S. E., Boucher O., Brovkin V., Byun Y. -H., Dix M., Gualdi S., Guo H., John J. G., Kharin S., Kim Y., Koshiro T., Ma L., Olivié D., Panickal S., Qiao F., Rong X., Rosenbloom N., Schupfner M., Séférian R., Sellar A., Semmler T., Shi X., Song Z., Steger C., Stouffer R., Swart N., Tachiiri K., Tang Q., Tatebe H., Voldoire A., Volodin E., Wyser K., Xin X., Yang S., Yu Y., Ziehn T. (2021): Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6. Earth Syst. Dyn., 12: 253-293. |
| References_xml | – reference: Abadi M., Agarwal A., Barham P., Brevdo E., Chen Z., Citro C., Corrado G. S., Davis A., Dean J., Devin M., Ghemawat S., Goodfellow I. J., Harp A., Irving G., Isard M., Jia Y., Józefowicz R., Kaiser L., Kudlur M., Levenberg J., Mané D., Monga R., Moore S., Murray D. G., Olah C., Schuster M., Shlens J., Steiner B., Sutskever I., Talwar K., Tucker P. A., Vanhoucke V., Vasudevan V., Viégas F. B., Vinyals O., Warden P., Wattenberg M., Wicke M., Yu Y., Zheng X. (2015): TensorFlow: Large-scale machine learning on heterogeneous systems, Software available from tensorflow.org. – reference: Sakamoto T. (2016): Computational research on mobile pastoralism using agent-based modeling and satellite imagery. PLoS ONE, 11: e0151157. – reference: Tebaldi C., Debeire K., Eyring V., Fischer E., Fyfe J., Friedlingstein P., Knutti R., Lowe J., O’Neill B., Sanderson B., van Vuuren D., Riahi K., Meinshausen M., Nicholls Z., Tokarska K. B., Hurtt G., Kriegler E., Lamarque J. -F., Meehl G., Moss R., Bauer S. E., Boucher O., Brovkin V., Byun Y. -H., Dix M., Gualdi S., Guo H., John J. G., Kharin S., Kim Y., Koshiro T., Ma L., Olivié D., Panickal S., Qiao F., Rong X., Rosenbloom N., Schupfner M., Séférian R., Sellar A., Semmler T., Shi X., Song Z., Steger C., Stouffer R., Swart N., Tachiiri K., Tang Q., Tatebe H., Voldoire A., Volodin E., Wyser K., Xin X., Yang S., Yu Y., Ziehn T. (2021): Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6. Earth Syst. Dyn., 12: 253-293. – reference: Hajima T., Watanabe M., Yamamoto A., Tatebe H., Noguchi M. A., Abe M., Ohgaito R., Ito A., Yamazaki D., Okajima H., Ito A., Takata K., Ogochi K., Watanabe S., Kawamiya M. (2020): Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks. Geosci. Model Dev., 13: 2197-2244. – reference: Tachiiri K., Komiyama H., Morinaga Y., Shinoda M. (2017): Application of a livestock weight model to the 2009-2010 winter disaster in Mongolia. Rangel. J., 39: 263-277. – reference: Begzsuren S., Ellis J. E., Ojima D. S., Coughenour M. B., Chuluun T. (2004): Livestock responses to drought and severe winter weather in the Gobi Three Beauty National Park, Mongolia. J. Arid Environ., 59: 785-796. – reference: Dong C., Loy C. C., He K., Tang X. (2014): Image Super-Resolution using Deep Convolutional Networks. https://doi.org/10.48550/arXiv.1501.00092 – reference: Ito A., Oikawa T. (2002): A simulation model of the carbon cycle in land ecosystems (Sim-CYCLE): a description based on dry-matter production theory and plot-scale validation. Ecol. Model., 151: 147-179. – reference: O’Neill B. C., Tebaldi C., van Vuuren D. P., Eyring V., Friedlingstein P., Hurtt G., Knutti R., Kriegler E., Lamarque J. -F., Lowe J., Meehl G. A., Moss R., Riahi K., Sanderson B. M. (2016): The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9: 3461-3482. – reference: Sternberg T. (2010): Unravelling Mongolia’s extreme winter disaster of 2010. Nomadic Peoples, 14: 72-86. – reference: Tachiiri K., Shinoda M. (2012): Quantitative risk assessment for future meteorological disasters: reduced livestock mortality in Mongolia. Clim. Change, 113: 867-882. – reference: Komiyama H. (2005): Actual damage to Mongolian animal husbandry due to the 2000-2002 dzud disaster. Bull. Jpn. Assoc. Mongolian Studies, 35: 73-85. – reference: Komiyama H. (2013): Looking back on the 2010 dzud (cold and snow disaster) in Mongolia. Nihon to Mongol (Japan and Mongolia), 126: 33-38. – reference: Tachiiri K., Shinoda M., Klinkenberg B., Morinaga Y. (2008): Assessing Mongolian snow disaster risk using livestock and satellite data. J. Arid Environ., 72: 2251-2263. – reference: Moritz M., Hamilton I. M., Yoak A. J., Scholte, P., Cronley J., Maddock, P., Pi H. Y. (2015): Simple movement rules result in ideal free distribution of mobile pastoralists. Ecol. Modell., 305: 54-63. |
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| SubjectTerms | Earth system model future projection livestock weight model Mongolia nomadism モンゴル 地球システムモデル 家畜体重モデル 将来予測 遊牧 |
| Title | Future vegetation projection by Earth system model and its impact on nomadism |
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