Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
EN
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| URI: | http://purl.tuc.gr/dl/dias/C440528C-BDDF-4319-85B1-61581CEDAFE8 | ||
| Έτος | 2020 | ||
| Τύπος | Κεφάλαιο σε Βιβλίο | ||
| Άδεια Χρήσης |
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| Βιβλιογραφική Αναφορά | S. Suman, M.R. North, G. P. Petropoulos, P.K. Srivastava, D.T. Hristopulos, D. S. Fuzzo, and T. N. Carlson, "Modelling key parameters characterising land surface using the SimSphere SVAT model," in Agricultural Water Management : Theories and Practices, P.K. Srivastava, M. Gupta, G. Tsakiris, N.W. Quinn, Eds. Cambridge, Massachusetts: Academic Press, 2020. pp. 409-442. doi: 10.1016/B978-0-12-812362-1.00020-5 https://doi.org/10.1016/B978-0-12-812362-1.00020-5 | ||
| Εμφανίζεται στις Συλλογές |
The present study investigates the ability of SimSphere, a soil vegetation atmosphere transfer model, to predict key parameters in characterising land surface interactions. In particular, the model's performance in predicting Net Radiation (Rnet), Latent Heat (LE) and Sensible Heat (H) was examined. For this purpose, concurrent in-situ measurements of the corresponding parameters for a total of 70 days of the year 2011 from seven CarboEurope network sites were acquired, incorporating a variety of environmental biomes and climatic conditions in the model evaluation. In overall, SimSphere was largely able to accurately predict the variables against which it was evaluated for most of the experimental sites. Statistical analysis showed highest agreement of H fluxes to the measured in-situ values for all ecosystems, with an average root mean square difference of 55.36 Wm−2. Predicted latent fluxes and Rnet also agreed well with the corresponding in-situ data with RSMDs of 62.75 and 64.65 Wm−2, respectively. Our findings contribute towards a better understanding of the model structure, functioning and its correspondence to the real-world system. They also further establish its capability as a useful teaching and research tool in modelling Earth's land surface interactions. This is important given its increasing use, including its synergies with Earth observation data.
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