Application of Copula Functions for Rainfall Interception Modelling

Abstract

Rainfall interception is an important process of the water cycle that can have significant influence on surface runoff and groundwater storage. Since rainfall interception measurements are rare and time consuming, rainfall interception estimation can be made indirectly using different meteorological variables. Experimental data of rainfall interception for birch and pine trees was measured at an experimental plot located in an urban area of Ljubljana, Slovenia in this study. A copula model was applied to predict the rainfall interception using meteorological variables, namely air temperature and vapour pressure deficit data. The copula model performance was compared to some other models such as decision trees, multiple linear regressions, and exponential functions. Using random sampling, we found that the copula model where Khoudraji-Liebscher copula functions were used yielded slightly smaller root mean square error (RMSE) and mean absolute error (MAE) values than other tested methods (i.e., RMSE and MAE results for birch trees were 24.2% and 18.2%, respectively and RMSE and MAE results for pine trees were 25.0% and 19.6%, respectively). The results demonstrate that the copula-based proposed method and other tested models could be used for the prediction of rainfall interception at the considered plot and in the wider surroundings. Furthermore, these models could also be applied for the prediction of rainfall interception for these two tree species in other locations under similar vegetation and meteorological conditions.

Joint Modeling of Precipitation and Temperature Using Copula Theory for Current and Future Prediction under Climate Change Scenarios in Arid Lands (Case Study, Kerman Province, Iran)

“…e presented results of joint modeling are in line with previously published research. For instance, Cong and Brady [25], Keerthirathne and Perera [18], and [50] indicated two Frank and Gaussian copulas for joint modeling of precipitation and temperature [48].…”

Nonparametric extrapolation of extreme quantiles: a comparison study

“…Later, Serinaldi (2011) implemented it to define semiparametric flow duration curves, annual flow duration curves, and their confidence intervals. Most recent works (e.g., Serinaldi et al 2012;Bezak et al 2017Bezak et al , 2018Bezak et al , 2019 took advantage of the method for copula-based models in hydrological modelling. A much wider dissemination has been experienced regarding kernel estimators in hydrological fields.…”

“…Another type of quantile estimator is the one proposed by Hutson (2002) and already applied in hydrology in the works of Serinaldi (2009Serinaldi ( , 2011Serinaldi et al 2012) and Bezak et al (2017Bezak et al ( , 2018Bezak et al ( , 2019. This method is also at the base of the semiparametric tail-extrapolated quantile estimators proposed by Wei et al (2015).…”

Modeling Temperature and Precipitation in Hyderabad and Medak Using Copula

“…This study was carried out in Agartala (humid region) and Bikener (Arid region). Lazoglou and Anagnostopoulou (4) developed a joint distribution for the above two factors using Copula in Mediterranean region.The remaining studies carried out by Bezak et al (5) in Slovenia, Yu et al (6) in China, Shaukat et al (7) in Pakistan, have also used Copula models for their study in which Rainfall is the main factor. Dzupire et al (8) , has used the dame Copula analysis to identify interdependency pattern between Temperature and Rainfall.…”