Models for estimation of hourly soil temperature at 5 cm depth and for degree-day accumulation from minimum and maximum soil temperature
Brian HortonTasmanian Institute of Agriculture, University of Tasmania, PO Box 46, Kings Meadows, Tas. 7249, Australia. Email: brian.horton@utas.edu.au
Soil Research 50(6) 447-454 https://doi.org/10.1071/SR12165
Submitted: 29 February 2012 Accepted: 8 August 2012 Published: 19 September 2012
Abstract
A model has been developed for the daily variation in soil temperature at 5 cm depth, for use where both the minimum and maximum temperatures are known or can be estimated. The model is based on data from three Australian sites with minute-by-minute data over 3–7 years. The model uses two sine curves; one for the increase from minimum to maximum and another for the relatively rapid decrease in temperature immediately after the maximum. An exponential decay function is used for the slower decrease in temperature until the minimum is reached.
The time of the minimum soil temperature is primarily determined by the time of sunrise and therefore varies depending on the day of the year, whereas the time of the maximum temperature is influenced primarily by the time of the middle of the day (midpoint between sunrise and sunset). The time of the transition point between the maximum and the next minimum is related to the time of sunset. Therefore, the model uses latitude, longitude, and the day of the year to determine the time of sunrise and sunset to adjust the shape of the temperature profile throughout the day.
The model has been validated using 3-hourly soil temperature data for 35 other sites in Australia, with a correlation of 0.993 between actual 3-hourly temperatures and those predicted. Its use for degree-day calculations has been validated using hourly data from a site in Victoria, where the model’s estimates of degree-days differ <0.7% from the value based on individual hourly temperatures, whereas methods that assume a symmetrical change from maximum to minimum temperature overestimate degree-days by 6–7%.
Additional keywords: daily temperature profile, air temperature.
References
Bryant SR, Thomas CD, Bale JS (2002) The influence of thermal ecology on the distribution of three nymphalid butterflies. Journal of Applied Ecology 39, 43–55.| The influence of thermal ecology on the distribution of three nymphalid butterflies.Crossref | GoogleScholarGoogle Scholar |
Cesaraccio C, Spano D, Duce P, Snyder RL (2001) An improved model for determining degree-day values from daily temperature data. International Journal of Biometeorology 45, 161–169.
| An improved model for determining degree-day values from daily temperature data.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD38%2Fktlansw%3D%3D&md5=335b23aee3bd9158dae866b910156dc4CAS |
Dallwitz R (1984) The influence of constant and fluctuating temperatures on development rate and survival of pupae of the Australian sheep blowfly Lucilia cuprina. Entomologia Experimentalis et Applicata 36, 89–95.
| The influence of constant and fluctuating temperatures on development rate and survival of pupae of the Australian sheep blowfly Lucilia cuprina.Crossref | GoogleScholarGoogle Scholar |
De Cat S, Larsen JWA, Anderson N (2012) Survival over winter and spring emergence of Lucilia cuprina (Diptera: Calliphoridae) in south-eastern Australia. Australian Journal of Entomology 51, 1–11.
Goudriaan J, van Laar HH (1994) ‘Modelling potential crop growth processes: textbook with exercises.’ (Kluwer Academic Publishers: Dordrecht)
He J, Li H, Kuhn NJ, Wang Q, Zhang X (2010) Effect of ridge tillage, no-tillage, and conventional tillage on soil temperature, water use, and crop performance in cold and semi-arid areas in Northeast China. Australian Journal of Soil Research 48, 737–744.
| Effect of ridge tillage, no-tillage, and conventional tillage on soil temperature, water use, and crop performance in cold and semi-arid areas in Northeast China.Crossref | GoogleScholarGoogle Scholar |
Horton B, Corkrey R (2011) A weighted coefficient model for estimation of Australian daily soil temperature at depths of 5 cm to 100 cm based on air temperature and rainfall. Soil Research 49, 305–314.
| A weighted coefficient model for estimation of Australian daily soil temperature at depths of 5 cm to 100 cm based on air temperature and rainfall.Crossref | GoogleScholarGoogle Scholar |
Nautical Almanac Office (1990) ‘Almanac for computers.’ (United States Naval Observatory: Washington, DC)
Parton WJ, Logan JA (1981) A model for diurnal temperature variation in soil and air temperatures. Agricultural Meteorology 23, 205–216.
| A model for diurnal temperature variation in soil and air temperatures.Crossref | GoogleScholarGoogle Scholar |
Roltsch WJ, Zalom FG, Strawn AJ, Strand JF (1999) Evaluation of several degree-day estimation methods in Californian climates. International Journal of Biometeorology 42, 169–176.
| Evaluation of several degree-day estimation methods in Californian climates.Crossref | GoogleScholarGoogle Scholar |
Uchida Y, Clough TJ, Kelliher FM, Sherlock RR (2010) Soil microbial respiration responses to changing temperature and substrate availability in fertile grassland. Australian Journal of Soil Research 48, 395–403.
| Soil microbial respiration responses to changing temperature and substrate availability in fertile grassland.Crossref | GoogleScholarGoogle Scholar |
University of California (2003) How to manage pests. Degree-days. Available at: www.ipm.ucdavis.edu/WEATHER/ddconcepts.html
Vogt WG, Bedo D (2001) A preliminary weather-driven model for estimating the seasonal phenology and abundance of Lucilia cuprina. In ‘FLICS Conference’. Launceston, Tas. pp. 62–64. (Tasmanian Institute of Agricultural Research, University of Tasmania: Hobart)
Watson CL (1980) Seasonal soil temperature regimes in south-eastern Australia. Australian Journal of Soil Research 18, 325–331.
| Seasonal soil temperature regimes in south-eastern Australia.Crossref | GoogleScholarGoogle Scholar |
Watson DM, Beattie GAC (1996) Degree-day models in New South Wales: climatic variation in the accuracy of different algorithms and geographical bias correction procedures. Australian Journal of Experimental Agriculture 36, 717–729.
| Degree-day models in New South Wales: climatic variation in the accuracy of different algorithms and geographical bias correction procedures.Crossref | GoogleScholarGoogle Scholar |
Wouter H (2011) Calculate sunset/sunrise time. Available at: www.codeproject.com/KB/cs/SunTime.aspx
