Performance of the physically-based Sparse Gash model for estimating rainfall interception of the Hyrcanian broad-leaved forests

Document Type : Research Paper

Authors

1 Forestry and Forest Economics Department, Faculty of Natural Resources,, University of Tehran

2 Forestry and Forest Economics Department,, Faculty of Natural Resources, University of Tehran

3 Department of Wood Sciences, Karaj Branch, Islamic Azad University, Karaj, I. R. Iran

4 Department of Forestry and Forest Economics, University of Tehran

Abstract

The difficulties in the measurement of rainfall interception in forests confirm the necessity of presenting models. The widely used models for estimating rainfall interception are physical-based models, among which the Sparse Gash is the most commonly used. We evaluated the Sparse Gash model for estimating the rainfall interception of five forest stands (two chestnut-leaved oak stands, two oriental beech stands, and one velvet maple stand) in the Hyrcanian region. In each stand, the gross rainfall and throughfall were measured using 5 and 20 rainfall collectors, respectively, and rainfall interception was calculated by subtracting the throughfall from gross rainfall. To evaluate the performance of the model, we used statistical metrics: Error percentage (Error), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Model Efficiency coefficient (CE). Based on the Pearson correlation coefficient, the correlation between the values estimated by the model and the observed values was statistically significant at a 95% confidence interval. In all forests, the values of the CE were higher than 0.5, indicating the appropriate efficiency of the model. Based on the Error, the model showed good capability in estimating the rainfall interception of four forest stands (i.e., oriental beech in Lajim, chestnut-leaved oak in Kohmiyan and Sari, and velvet maple in Sari Error metric were found to be -10.3%, +12.7%, +10.8%, and +15.4%, respectively). Studying the performance of physically-based models in forests with different species and different allometric, climatic and rainfall characteristics completes the information gap about the efficiency of models to estimate rainfall interception.

Keywords

References

[1]. Muzylo, A., Llorens, P., Valente, F., Keizer, J.J., Domingo, F., and Gash, J.H.C. (2009). A review of rainfall interception modelling. Journal of hydrology, 370(1-4): 191-206.
[2]. Sadeghi, S.M.M., Gordon, D.A., and Van Stan II, J.T. (2020). A Global Synthesis of Throughfall and Stemflow Hydrometeorology. In Precipitation Partitioning by Vegetation (pp. 49-70). Springer, Cham.
[3]. Sadeghi, S.M.M., Attarod, P., Van Stan, J.T., and Pypker, T.G. (2016). The importance of considering rainfall partitioning in afforestation initiatives in semiarid climates: A comparison of common planted tree species in Tehran, Iran. Science of the Total Environment, 568: 845-855.
[4]. Sun, X., Onda, Y., Kato, H., Gomi, T., and Komatsu, H. (2015). Effect of strip thinning on rainfall interception in a Japanese cypress plantation. Journal of Hydrology, 525: 607-618.
[5]. Gash, J., Lloyd, C., and Lachaud, G. 1995. Estimating sparse forest rainfall interception with an analytical model. Journal of Hydrology, 170: 79-86.
[6]. Lloyd, C.R., Gash, J.H., and Shuttleworth, W.J. (1988). The measurement and modelling of rainfall interception by Amazonian rain forest. Agricultural and Forest Meteorology, 43(3-4): 277-294.
[7]. Sadeghi, S.M.M., Attarod, P., Van Stan II, J.T., Pypker, T.G., and Dunkerley, D. (2015). Efficiency of the reformulated Gash's interception model in semiarid afforestations. Agricultural and Forest Meteorology, 201: 76-85.
[8]. Fathizadeh, O., Hosseini, S.M., Keim, R.F., and Boloorani, A.D. (2018). A seasonal evaluation of the reformulated Gash interception model for semi-arid deciduous oak forest stands. Forest Ecology and Management, 409: 601-613.
[9]. de Carvalho Lopes, D., Neto, A.J.S., de Queiroz, M.G., de Souza, L.S.B., Zolnier, S., and da Silva, T.G.F. (2020). Sparse Gash model applied to seasonal dry tropical forest. Journal of Hydrology, 590: 125497.
[10]. Li, Y., Liu, X., Zhang, C., Li, Z., Zhao, Y., and Niu, Y. (2020). Effect of initial plant density on modeling accuracy of the revised sparse Gash model: a case study of Pinus tabuliformis plantations in northern China. Hydrology Research, 51(5): 1170-1183.
[11] Motahari, M., Attarod, P., Pypker, T.G., Etemad, V., and Shirvany, A. (2013). Rainfall interception in a Pinus eldarica in a semi-arid climate: An application of the Gash model. Journal of Agricultural Sceicne and Technology, 15(5): 981-994.
[12]. Sadeghi, S.M.M., Van Stan II, J.T., Pypker, T.G., and Friesen, J. (2017). Canopy hydrometeorological dynamics across a chronosequence of a globally invasive species, Ailanthus altissima (Mill., tree of heaven). Agricultural and Forest Meteorology, 240: 10-17.
[13]. Sefidi, K., and Sadeghi, S.M.M. (2020). Comparison of revised Gash models for estimating rainfall interception in an oriental beech stand, west of Hyrcanian region. Iranian Journal of Forests (in-press).
[14]. Attarod, P., and Sadeghi, S.M.M. (2018). Forest Ecohydrology, Tehran: Jahad Daneshgahi.
[15]. Dawson, C.W., Abrahart, R.J., and See, L.M. (2007). HydroTest: a web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts. Environmental Modelling and Software, 22: 1034-1052.
[16]. Nazari, M., Chaichi, M.R., Kamel, H., Grismer, M., and Sadeghi, S.M.M. (2020). Evaluation of estimation methods for monthly reference evapotranspiration in arid climates. Arid Ecosystems, 10(4): 329-326.
[17]. Hennemuth, B., Bender, S., Bülow, K., Dreier, N., Keup-Thiel, E., Krüger, O., Mudersbach, C., Radermacher, C., and Schoetter, R. (2013). Statistical methods for the analysis of simulated and observed climate data, applied in projects and institutions dealing with climate change impact and adaptation, CSC Report 13, Climate Service Center, Hamburg, Germany, 135 p.
[18]. Dykes, A.P., 1997. Rainfall interception from a lowland tropical rainforest in Brunei. Journal of Hydrology, 200: 260-279.
[19]. Lankreijer, H., Lundberg, A., Grelle, A., Lindroth, A. and Seibert, J., 1999. Evaporation and storage of intercepted rain analysed by comparing two models applied to a boreal forest. Agricultural and Forest Meteorology, 98-99: 595-604.
[20]. Pypker, T.G., Bond, B.J., Link, T.E., Marks, D., and Unsworth, M.H. (2005). The importance of canopy structure in controlling the interception loss of rainfall: Examples from a young and an old-growth Douglas-fir forest. Agricultural and Forest Meteorology, 130: 113-129.
[21]. Sadeghi, S.M.M., Van Stan, J.T., Pypker, T.G., Tamjidi, J., Friesen, J., and Farahnaklangroudi, M. (2018). Importance of transitional leaf states in canopy rainfall partitioning dynamics. European Journal of Forest Research, 137: 121-130.
[22]. Tu, L., Xiong, W., Wang, Y., Yu, P., Liu, Z., Han, X., & Xu, L. (2021). Integrated effects of rainfall regime and canopy structure on interception loss: A comparative modelling analysis for an artificial larch forest. Ecohydrology, e2283.
[23]. Liu, Z., Wang, Y., Tian, A., Liu, Y., Webb, A.A., Wang, Y., Zho, H., Yu, P., Xiong, W., and Xu, L. (2018). Characteristics of canopy interception and its simulation with a revised Gash model for a larch plantation in the Liupan Mountains, China. Journal of Forestry Research, 29(1): 187-198.
[24]. Nazari, M., Sadeghi, S.M.M., Van Stan II, J.T., and Chaichi, M.R. (2020). Rainfall interception and redistribution by maize farmland in central Iran. Journal of Hydrology: Regional Studies, 27, 100656.
[25]. Ghimire, C.P., Bruijnzeel, L.A., Lubczynski, M.W., Ravelona, M., Zwartendijk, B.W., and van Meerveld, H.I. (2017). Measurement and modeling of rainfall interception by two differently aged secondary forests in upland eastern Madagascar. Journal of Hydrology, 545: 212-225.
Forest and Wood Products
Volume 74, Issue 3
December 2021
Pages 345-355

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