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Measurements and Regional Climate Modeling of Soil Temperature With and Without Bias Correction Method Under Arid Environment: Can Soil Temperature Outperform Air Temperature as a Climate Change Indicator?

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Abstract

Soil temperature plays a critical role in many soil functions, particularly in arid ecosystems, but is scarcely reported as potential indicator of climate change. Although studies have been conducted worldwide to investigate (both measurements and modeling) temperature changes in the soil profile in response to ambient temperature, no information on soil temperature is available in the state of Kuwait. Hydrological and many bio-geochemical processes are more sensitive to soil temperature than air temperature. In this study, we used observed soil temperature data (2007–2016) from three sites with three soil depth increments (5, 50, 100 cm) and compared to regional climate and regression models’ output. The most salient finding of this study is the tight association between observed and simulated soil temperature at shallower soil depths from both the regional climate model (RegCM4) and linear regression model. The RegCM4 model poorly predicted (underestimated) soil temperature at deeper soil layers relative to shallower soil layers. Application of the linear scaling (LS) method has significantly improved the RegCM4 model performance with respect to measured soil temperature, which allows an accurate evaluation of the impact of climate change on the soil temperature under different soil management systems. These findings indicate that RegCM4 can be applied as a reliable predictor of soil temperature under arid ecosystems for which there is a gap in site data availability.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Riedel, T. (2019). Temperature-associated changes in groundwater quality. Journal of Hydrology, 572, 206–212.

    Article  CAS  Google Scholar 

  2. Sen, C., & Ozturk, O. (2017). The relationship between soil moisture and temperature vegetation on Kirklareli City Luleburgaz District a natural pasture vegetation. International Journal of Environmental & Agriculture Research, 3, 21–29.

    Google Scholar 

  3. Jungqvist, G., Oni, S. K., Teutschbein, C., & Futter, M. N. (2014). Effect of climate change on soil temperature in Swedish boreal forests. PLoS ONE, 9(4), e93957.

    Article  Google Scholar 

  4. Qi, J., Li, S., Li, Q., Xing, Z., Bourque, C., & Meng, F. R. (2016). A new soil-temperature module for SWAT application in regions with seasonal snow cover. Journal of Hydrology, 538, 863–877.

    Article  Google Scholar 

  5. Ni, J., Cheng, Y., Wang, Q., Ng, C. W. W., & Garg, A. (2019). Effects of vegetation on soil temperature and water content: Field monitoring and numerical modelling. Journal of Hydrology, 571, 494–502.

    Article  Google Scholar 

  6. Zhang, H., Wang, E., Zhou, D., Zhongkui, L. Z., & Zhang, Z. (2016). Rising soil temperature in China and its potential ecological impact. Scientific Report, 6, 35530. https://doi.org/10.1038/srep35530

    Article  CAS  Google Scholar 

  7. Bullied, W. J., Flerchinger, G. N., Bullock, P. R., & Van Acker, R. C. (2014). Process-based modeling of temperature and water profiles in the seedling recruitment zone: Part I. Model validation. Agricultural and Forest Meteorology, 188, 89–103.

    Article  Google Scholar 

  8. Luzuriaga, A. L., Sánchez, A. M., Maestre, F. T., & Escudero, A. (2012). Assemblage of a semi-arid annual plant community: Abiotic and biotic filters act hierarchically. PLoS ONE, 7(7), e41270.

    Article  CAS  Google Scholar 

  9. Pal, J. S., & Eltahir, E. A. B. (2016). Future temperature in southwest Asia projected to exceed a threshold for human adaptability. Nature Climate Change, 6, 197–200.

    Article  Google Scholar 

  10. Davidson, E., & Janssens, I. (2006). Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature, 440, 165–173.

    Article  CAS  Google Scholar 

  11. Zhang, H., Yao, X., Zeng, W., Fang, Y., & Wang, W. (2020). Depth dependence of temperature sensitivity of soil carbon dioxide, nitrous oxide and methane emissions. Soil Biology and Biochemistry, 149, 107956.

    Article  CAS  Google Scholar 

  12. Li, L., Zheng, Z., Wang, W., Biederman, J. A., Xu, X., Ran, Q., Qian, R., Xu, C., Zhang, B., Wang, F., Zhou, S., Cui, L., Che, R., Hao, Y., Cui, X., Xu, Z., & Wang, Y. (2020). Terrestrial N2O emissions and related functional genes under climate change: A global meta-analysis. Global Change Biology, 26, 931–943.

    Article  Google Scholar 

  13. Mon, E. E., Hamamoto, S., Kawamoto, K., Komatsu, T., & Moldrup, P. (2016). Temperature effects on solute diffusion and adsorption in differently compacted kaolin clay. Environmental Earth Science, 75, 562–571.

    Article  Google Scholar 

  14. Araghi, A., Mousavi-Baygi, M., & Adamowski, J. (2017). Detecting Ts trends in Northeast Iran from 1993 to 2016. Soil Tillage Research, 174, 177–192.

    Article  Google Scholar 

  15. Brown, S. E., Pregitzer, K. S., Reed, D. D., & Burton, A. J. (2000). Predicting daily mean soil temperature from daily mean air temperature in four northern hardwood forest stands. Forest Science, 46, 297–301.

    Google Scholar 

  16. Yang, C. C., Prasher, O. S., & Mehuys, G. R. (1997). An artificial neural network to estimate soil temperature. Canadian Journal of Soil Science, 77, 421–429.

    Article  Google Scholar 

  17. Zhu, J., & Liang, X. Z. (2005). Regional climate model simulation of U.S. soil temperature and moisture during 1982–2002. Journal of Geophysical Research, 110, D24110. https://doi.org/10.1029/2005JD006472

    Article  Google Scholar 

  18. Godfrey, C. M., & Stensrud, D. J. (2007). Soil temperature and moisture errors in operational eta model analyses. Journal of Hydrometeorology, 9, 386–387.

    Google Scholar 

  19. Wang, L., Li, X., Chen, Y., & Yang, K. (2016). Validation of the global land data assimilation system based on measurements of soil temperature profiles. Agricultural and Forest Meteorology, 218–219, 288–297.

    Article  Google Scholar 

  20. Qian, J. H. (2008). Why precipitation is mostly concentrated over islands in the maritime continent. Journal of the Atmospheric Sciences, 65, 1428–1441.

    Article  Google Scholar 

  21. Qian, J. H., Robertson, A. W., & Moron, V. (2010). Interactions among ENSO, the monsoons, and diurnal cycle in rainfall variability over Java, Indonesia. Journal of the Atmospheric Sciences, 67, 3509–3524.

    Article  Google Scholar 

  22. Giorgi, F., & Mearns, L. O. (1999). Introduction to special section: Regional climate modeling revisited. Journal of Geophysical Research: Atmospheres, 104, 6335–6352.

    Article  Google Scholar 

  23. Giorgi, F., Pal, J. S., Bi, X., Sloan, L., Elguindi, N., & Solmon, F. (2006). Introduction to the TAC special issue: The RegCNET network. Theoretical Applied Climatology, 86, 1–4.

    Article  Google Scholar 

  24. Hu, Y. X., & Stamnes, K. (1993). An accurate parameterization of the radiative properties of water clouds suitable for use in climate models. Journal of Climate, 6, 728–742.

    Article  Google Scholar 

  25. Holtslag, A. A. M., & Boville, B. A. (1993). Local versus nonlocal boundary layer diffusion in a global model. Journal of Climate, 6, 1825–1842.

    Article  Google Scholar 

  26. Emanuel, K. A. (1991). A scheme for representing cumulus convection in large-scale models. Journal of the Atmospheric Sciences, 48(21), 2313–2335.

    Article  Google Scholar 

  27. Oleson, K. W., Dai, Y., Bonan, G., Bosilovich, M., et al. (2013). Technical description of the community land model. Boulder, CO: National Center for Atmospheric Research. Tech Note NCAR/TN-461+ STR, NCAR.

    Google Scholar 

  28. Anwar, S. A., Zakey, A. S., Robaa, S. M., & Wahab, M. M. (2019). The influence of two land-surface hydrology schemes on the regional climate of Africa using the RegCM4 model. Theoretical and Applied Climatology, 136, 1535. https://doi.org/10.1007/s00704-018-2556-8

    Article  Google Scholar 

  29. Anwar, S. A., & Hejabi, S. (2023). The influence of different initial conditions on the soil temperature profile of Egypt using a regional climate model. Engineering Proceedings, 31(1), 62. https://doi.org/10.3390/ASEC2022-1385

    Article  Google Scholar 

  30. Fang, G. H., Yang, J., Chen, Y. N., & Zammit, C. (2015). Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China. Hydrology Earth System Sciences, 19, 2547–2559. https://doi.org/10.5194/hess-19-2547

    Article  Google Scholar 

  31. Ngai, S. T., Tangang, F., & Juneng, L. (2017). Bias correction of global and regional simulated daily precipitation and surface mean temperature over Southeast Asia using quantile mapping method. Global Planetary change, 149, 79–90.

    Article  Google Scholar 

  32. Xu, Z., & Yang, Z. L. (2012). An improved dynamical downscaling method with GCM bias corrections and its validation with 30 years of climate simulations. Journal of Climate, 25, 6271–6286. https://doi.org/10.1175/JCLI-D-12-00005.1

    Article  Google Scholar 

  33. Luo, M., Liu, T., Meng, F., Duan, Y., Frankl, A., Bao, A., & Maeyer, P. D. (2018). Comparing bias correction methods used in downscaling precipitation and temperature from regional climate models: a case study from the Kaidu River Basin in Western China. Water, 10, 1046. https://doi.org/10.3390/w10081046

    Article  Google Scholar 

  34. Rocheta, E., Evans, J. P., & Sharma, A. (2017). Can bias correction of regional climate model lateral boundary conditions improve low-frequency rainfall variability. Journal of Climate, 30, 9785–9806.

    Article  Google Scholar 

  35. Mostafa, S. M., Anwar, S. A., Zakey, A. S., & Wahab, M. M. A. (2023). Bias-correcting the maximum and minimum air temperatures of Egypt using a high-resolution regional climate model (RegCM4). Engineering Proceedings, 31(1), 73. https://doi.org/10.3390/ASEC2022-1385

    Article  Google Scholar 

  36. Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., et al. (2011). The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society., 137, 553–597.

    Article  Google Scholar 

  37. Meng, X., Wang, H., Wu, Y., Long, A., Wang, J., Shi, C., & Ji, X. (2017). Investigating spatiotemporal changes of the land-surface processes in Xinjiang using high resolution REGCM43.5 and CLDAS: Soil temperature. Scientific Reports, 7, 13286.

    Article  Google Scholar 

  38. Tabari, H., Talaee, H. P., & Willems, P. (2015). Short-term forecasting of soil temperature using artificial neural network. Meteorological Applications, 22, 576–585.

    Article  Google Scholar 

  39. Bilgili, M. (2010). Prediction of Ts using regression and artificial neural network models. Meteorology and Atmospheric Physics, 110, 59–70.

    Article  Google Scholar 

  40. Zhang, Y., Chen, W., Smith, S. L., Riseborough, D. W., & Cihlar, J. (2005). Soil temperature in Canada during the twentieth century: Complex responses to atmospheric climate change. Journal of Geophysical Research: Atmospheres, 110, D03112. https://doi.org/10.1029/2004JD004910

    Article  Google Scholar 

  41. Fang, X., Luo, S., & Lyu, S. (2019). Observed soil temperature trends associated with climate change in the Tibetan Plateau, 1960–2014. Theoretical and Applied Climatology, 135, 169–181.

    Article  Google Scholar 

  42. Araghi, A., Adamowski, J., Martinez, C. J., & Olesen, J. E. (2019). Projections of future Ts in northeast Iran. Geotherma, 349, 11–24.

    Google Scholar 

  43. Houle, D., Bouffard, A., Duchesne, L., Logan, T., & Harvey, R. (2012). Projections of future soil temperature and water content for three Southern Quebec forested sites. Journal of Climate, 25, 7690–7701.

    Article  Google Scholar 

  44. Bradford, J. B., Schlaepfer, D. R., Lauenroth, W. K., Palmquist, K. A., Chambers, J. C., Maestas, J. D., & Campbell, S. B. (2019). Climate-driven shifts in Ts and moisture regimes suggest opportunities to enhance assessments of dryland resilience and resistance. Frontiers in Ecology and Evolution, 7, 358. https://doi.org/10.3389/fevo.2019.00358

    Article  Google Scholar 

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Acknowledgements

The earth system physics (ESP) of the ICTP institute is acknowledged for providing the ERA-Interim dataset for conducting the simulation, which can be obtained from http://www.clima-dods.ictp.it/regcm4/. The source code of the RegCM-4.7.0 model can be downloaded from https://github.com/ICTP/RegCM/. We are also very grateful to Dr. Alfred Anderson, Associate Professor, Department of Food and Nutrition Sciences, Kuwait University, for proofreading the article and providing insightful comments. Similarly, we recognize the time and efforts of reviewers (anonymous) whose comments and constructive criticism helped sharpen the focus of the article.

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Authors and Affiliations

  1. Department of Environmental Sciences, College of Life Sciences, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait, Kuwait

    Abdirashid Elmi

  2. Egyptian Meteorological Authority, Qobry EL-Kobba, P.O. Box 11784, Cairo, Egypt

    Samy A. Anwar

  3. Department of Meteorology, Directorate General of Civil Aviation, Kuwait, Kuwait

    Hassan Al-Dashti

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  1. Abdirashid Elmi
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Contributions

AE prepared the manuscript from data analysis and graphing to overall writing up of the manuscript; SA performed the regional model; HD collected the field soil temperature data. All authors read and approved the final manuscript.

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Correspondence to Abdirashid Elmi.

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Elmi, A., Anwar, S.A. & Al-Dashti, H. Measurements and Regional Climate Modeling of Soil Temperature With and Without Bias Correction Method Under Arid Environment: Can Soil Temperature Outperform Air Temperature as a Climate Change Indicator?. Environ Model Assess (2023). https://doi.org/10.1007/s10666-023-09945-7

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