Quantify the impacts of four land cover types on surface temperature in the Heihe River Basin
Received date: 2022-06-01
Revised date: 2022-07-11
Online published: 2023-02-24
Micrometeorological observations at four sites in the Heihe River basin from June to September 2012 are used to evaluate the direct decomposed temperature metric (DTM) theory and the intrinsic biophysical mechanism (IBPM), as well as to investigate the biophysical effects of land use and land cover change on surface temperature. Through the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project, the four sites were outfitted with Eddy Covariance Systems and other conventional weather instruments. The desert has the highest land surface temperature both daytime and nighttime. Compared to the desert site, the non-desert sites have average surface cooling effects of -17.8 K and -1.8 K during daytime and nighttime. Both the DTM and IBPM theories are founded on the surface energy balance equation; however, the energy balance ratios at the four sites range between 80% and 90% during the day and less than 30% at night. To revise the two theories, we distribute the imbalance term to the sensible and latent heat fluxes in proportion to the Bowen ratio. The biophysical effects of different types of land on surface temperature are then investigated by comparing the quantitative results of the two revised theories. The calculated surface temperature of DTM theory and IBPM theory agrees well with the observed results after forcing the energy balance closure to the fluxes, especially at night. The revised IBPM theory matches the observed results better than the revised DTM theory. The revised IBPM results show that the non-radiative effect related to the partitioning of available energy plays a significant role in the daytime cooling effect of non-desert sites. Changes in aerodynamic roughness (mean -4.97 K) and Bowen ratio (mean -2.43 K) both contribute a cooling signal during the day, and these cooling effects even outweigh the warming effects of the radiation term (mean +5.21 K). At night, the direct biophysical effects are weaker than during the day, and the indirect effects of the atmospheric background can even offset the direct biophysical effects.
Erchen LI , Yu ZHANG , Guanghui YUAN . Quantify the impacts of four land cover types on surface temperature in the Heihe River Basin[J]. Arid Zone Research, 2023 , 40(1) : 30 -38 . DOI: 10.13866/j.azr.2023.01.04
[1] | 刘婉如, 陈春波, 罗格平, 等. 巴尔喀什湖流域土地利用/覆被变化过程与趋势[J]. 干旱区研究, 2021, 38(5): 1452-1463. |
[1] | [Liu Wanru, Chen Chunbo, Luo Geping, et al. Change processes and trends of land use/cover in the Balkhash Lake basin[J]. Arid Zone Research, 2021, 38(5): 1452-1463.] |
[2] | Lee X, Goulden M, Hollinger D, et al. Observed increase in local cooling effect of deforestation at higher latitudes[J]. Nature, 2011, 479(7373): 384-387. |
[3] | Juang J, Katul G, Siqueira M, et al. Separating the effects of albedo from eco-physiological changes on surface temperature along a successional chronosequence in the southeastern United States[J]. Geophysical Research Letters, 2007, 34(21): L21408. doi: 10.1029/2007GL031296. |
[4] | Zhang M, Lee X, Yu G, et al. Response of surface air temperature to small-scale land clearing across latitudes[J]. Environmental Research Letters, 2014, 9(3): 034002. |
[5] | Baldocchi D, Ma S. How will land use affect air temperature in the surface boundary layer? Lessons learned from a comparative study on the energy balance of an oak savanna and annual grassland in California, USA[J]. Tellus B: Chemical and Physical Meteorology, 2013, 65(1): 19994. |
[6] | Betts A, Desjardins R, Worth D, et al. Impact of land use change on the diurnal cycle climate of the Canadian Prairies[J]. Journal of Geophysical Research: Atmospheres, 2013, 118(21): 11996-12011. |
[7] | Zhao K, Jackson R. Biophysical forcings of land-use changes from potential forestry activities in North America[J]. Ecological Monographs, 2014, 84(2): 329-353. |
[8] | Broucke S, Luyssaert S, Davin E, et al. New insights in the capability of climate models to simulate the impact of LUC based on temperature decomposition of paired site observations[J]. Journal of Geophysical Research: Atmospheres, 2015, 120(11): 5417-5436. |
[9] | Luyssaert S, Jammet M, Stoy P, et al. Land management and land-cover change have impacts of similar magnitude on surface temperature[J]. Nature Climate Change, 2014, 4: 389-393. |
[10] | Claussen M, Brovkin V, Ganopolski A. Biogeophysical versus biogeochemical feedbacks of large-scale land cover change[J]. Geophysical Research Letters, 2001, 28(6): 1011-1014. |
[11] | Bounoua L, DeFries R, Collatz G, et al. Effects of land cover conversion on surface climate[J]. Climate Change, 2002, 52: 29-64. |
[12] | Campra P, Garcia M, Canton Y, et al. Surface temperature cooling trends and negative radiative forcing due to land use change toward greenhouse farming in southeastern Spain[J]. Journal of Geophysical Research: Atmospheres, 2008, 113(D18): 1044. |
[13] | Kueppers L, Snyder M, Sloan L, et al. Irrigation cooling effect: Regional climate forcing by land-use change[J]. Geophysical Research Letters, 2007, 34(3): 407-423. |
[14] | Lobell D, Bala G, Duffy P. Biogeophysical impacts of cropland management changes on climate[J]. Geophysical Research Letters, 2006, 33(6): 272-288. |
[15] | Zhang Y, Liu H, Foken T, et al. Coherent structures and flux contribution over an inhomogeneously irrigated cotton field[J]. Theoretical and Applied Climatology, 2011, 103: 119-131. |
[16] | Adegoke J, Roger S, Eastman J, et al. Impact of irrigation on midsummer surface fluxes and temperature under dry synoptic conditions: A regional atmospheric model study of the U. S. high plains[J]. Monthly Weather Review, 2003, 131(3): 556-564. |
[17] | Kalnay E, Cai M. Impact of urbanization and land use on climate change[J]. Nature, 2003, 423(6939): 528-531. |
[18] | McCarthy M, Best M, Betts R. Climate change in cities due to global warming and urban effects[J]. Geophysical Research Letters, 2010, 37(9): 232-256. |
[19] | Zhao L, Lee X, Smith R. et al. Strong contributions of local background climate to urban heat islands[J]. Nature, 2014, 511: 216-219. doi. org/10.1038/nature13462. |
[20] | Basara J, Hall P, Schroeder A, et al. Diurnal cycle of the Oklahoma City urban heat island[J]. Journal of Geophysical Research: Atmospheres, 2008, 113: D20109. |
[21] | Lin S, Feng J, Wang J, et al. Modeling the contribution of long-term urbanization to temperature increase in three extensive urban agglomerations in China[J]. Journal of Geophysical Research: Atmospheres, 2016, 121(4): 1683-1697. |
[22] | Burakowski E, Tawfik A, Ouimette A, et al. The role of surface roughness, albedo, and Bowen ratio on ecosystem energy balance in the Eastern United States[J]. Agricultural and Forest Meteorology, 2018, 249(28): 367-376. |
[23] | Wang L, Lee X, Schultz N, et al. Response of surface temperature to afforestation in the Kubuqi Desert, Inner Mongolia[J]. Journal of Geophysical Research: Atmospheres, 2018, 123(2): 948-964. |
[24] | Zhao L, Lee X, Smith R, et al. Strong contributions of local background climate to urban heat islands[J]. Nature, 2014, 511(7508): 216-219. |
[25] | Chen L, Dirmeyer P A. Adapting observationally based metrics of biogeophysical feedbacks from land cover/land use change to climate modeling[J]. Environmental Research Letters, 2016, 11(3): 034002. doi: 10.1088/1748-9326/11/3/034002. |
[26] | Davin E, Noblet-Ducoudré N. Climatic impact of global-scale deforestation: Radiative versus nonradiative processes[J]. Journal of Climate, 2010, 23(1): 97-112. |
[27] | Bonan G, Pollard D, Thompson S. Effects of boreal forest vegetation on global climate[J]. Nature, 1992, 359(6397): 716-718. |
[28] | Bright R, Davin E, O’Halloran T, et al. Local temperature response to land cover and management change driven by non-radiative processes[J]. Nature Climate Change, 2017, 7(4): 296-302. |
[29] | Rigden A, Li D. Attribution of surface temperature anomalies induced by land use and land cover changes[J]. Geophysical Research Letters, 2017, 44(13): 6814-6822. |
[30] | Perugini L, Caporaso L, Marconi S, et al. Biophysical effects on temperature and precipitation due to land cover change[J]. Environmental Research Letters, 2017, 12(5): 053002. |
[31] | Peng S, Piao S, Zeng Z, et al. Afforestation in China cools local land surface temperature[J]. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(8): 2915-2919. |
[32] | 曹永香, 毛东雷, 薛杰, 等. 绿洲-沙漠过渡带植被覆盖动态变化及其驱动因素——以新疆策勒为例[J]. 干旱区研究, 2022, 39(2): 510-521. |
[32] | [Cao Yongxiang, Mao Donglei, Xue Jie, et al. Dynamic changes and driving factors of vegetation cover in the oasis-desert ecotone: A case study of Cele, Xinjiang[J]. Arid Zone Research, 2022, 39(2): 510-521.] |
[33] | Duveiller G, Hooker J, Cescatti A. The mark of vegetation change on Earth’s surface energy balance[J]. Nature Communication, 2018, 9: 679. doi: 10.1038/s41467-017-02810-8. |
[34] | Li Y, Zhao M, Motesharrei S. et al. Local cooling and warming effects of forests based on satellite observations[J]. Nature Communication, 2015, 6: 6603. doi: 10.1038/ncomms7603. |
[35] | Schultz N, Lawrence P, Lee X. Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation[J]. Journal of Geophysical Research Biogeosciences, 2017, 122(4): 903-917. |
[36] | Ge J, Guo W, Pitman A, et al. The nonradiative effect dominates local surface temperature change caused by afforestation in China[J]. Journal of Climate, 2019, 32(14): 4445-4471. |
[37] | Williams M, Richardson A, Reichstein M, et al. Improving land surface models with FLUXNET data[J]. Biogeosciences, 2009, 6(7): 1341-1359. |
[38] | Xu Z, Liu S, Li X, et al. Intercomparison of surface energy flux measurement systems used during the HiWATER-MUSOEXE[J]. Journal of Geophysical Research: Atmospheres, 2013, 118(23): 13140-13157. |
[39] | Li X, Cheng G, Liu S, et al. Heihe watershed allied telemetry experimental research (HiWATER): Scientific objectives and experimental design[J]. Bulletin of American Meteorological Society, 2013, 94(8): 1145-1160. |
[40] | Twine T, Kustas W, Norman J, et al. Correcting eddy-covariance flux underestimates over a grassland[J]. Agricultural and Forest Meteorology, 2000, 103(3): 279-300. |
[41] | 阳坤, 王介民. 一种基于土壤温湿资料计算地表土壤热通量的温度预报校正法[J]. 中国科学: 地球科学, 2008, 38(2): 243-250. |
[41] | [Yang Kun, Wang Jiemin. A temperature prediction-correction method for estimating surface soil heat flux from soil temperature and moisture data[J]. Scientia Sinica(Terrae), 2008, 38(2): 243-250.] |
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