scholarly journals A new transfer function model for the estimation of non-point-source solute travel times

2021 ◽  
Vol 598 ◽  
pp. 126157
Author(s):  
Marialaura Bancheri ◽  
Antonio Coppola ◽  
Angelo Basile
2020 ◽  
Author(s):  
Marialaura Bancheri ◽  
Antonio Coppola ◽  
Angelo Basile

<p>Transfer functions are travel time probability density functions (TT pdfs), which describe the leaching behaviour in a given soil profile. Once they are defined, the output solute concentration at a given time and depth is simply the transfer function convolution with the input concentration signal to the system.</p><p>In this work we propose an extended version of Jury's transfer function model (TFM-ext). The proposed model allows to simulate the spatio-temporal distribution of nonpoint-source solutes along the unsaturated zone that: i) integrates a simplified statistical approach with the physically-based soil hydrological parameters; ii) is valid for wide range of applications, both in space and time; iii) is standard and easily replicable; iv) is easy to interpret.</p><p>With the assumptions of a) a gravity induced water flow, b) a conservative and nonreactive solute and c) a purely convective flow, ignoring the convective mixing of solute flowing at different velocities and the molecular diffusion, the TT pdf were calculated as functions of the unsaturated hydraulic conductivity k(θ). The strength of the model, despite its important assumptions, is that it derives the TT pdf from a physical quantity, i.e. the hydraulic conductivity function. Moreover, the model extends the transport process to the generic depth z, where information on the hydraulic properties could not be available, assuming a lognormal travel time pdf, whose parameters are scaled according to the generalized transfer function model.</p><p>A sensitivity analysis, based on Monte Carlo simulations, to evaluate to which parameters the TFM-ext is more sensitive, was performed. Results shown that θ<sub>s </sub>and τ, of the van Genuchten-Mualem model, are the parameter affecting more the mean travel times.</p><p>Moreover, in order to validate TFM-ext, an application in the Telesina Valley, a hilly area of 200 km<sup>2</sup> in Southern Italy, was performed. Forty-six soil profiles, completely characterized from the hydrological point of view, were used to evaluate the mean travel times and then compared with the results obtained with a notable physically based model, Hydrus 1D. Two distinct applications were performed: the first with constant upper boundary conditions equal to those applied to the TFM-ext exercise, and the second with real daily variable upper boundary conditions. Results of both cases gave very high correlation coefficients (above 0.8) and mean absolute errors of 30 and 40 days, respectively.</p><p>Eventually, the model was implemented as an operative tool for the groundwater vulnerability assessment within the geospatial Decision Support System developed for LANDSUPPORT H2020 project.</p>


2019 ◽  
Vol 7 (3) ◽  
Author(s):  
Nur Laela Fitriani ◽  
Pika Silvianti ◽  
Rahma Anisa

Transfer function model with multiple input is a multivariate time series forecasting model that combines several characteristics of ARIMA models by utilizing some regression analysis properties. This model is used to determine the effect of output series towards input series so that the model can be used to analyze the factors that affect the Jakarta Islamic Index (JII). The USD exchange rate against rupiah and Dow Jones Index (DJI) were used as input series. The transfer function model was constructed through several stages: model identification stage, estimation of transfer function model, and model diagnostic test. Based on the transfer function model, the JII was influenced by JII at the period of one and two days before. JII was also affected by the USD exchange rate against rupiah at the same period and at one and two days before. In addition, the JII was influenced by DJI at the same period and also at period of one until five days ago. The Mean Absolute Prencentage Error (MAPE) value of forecasting result was 0.70% and the correlation between actual and forecast data was 0.77. This shows that the model was well performed for forecasting JII.


Economies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 21
Author(s):  
Jazmín González Aguirre ◽  
Alberto Del Villar

This paper seeks to assess the effectiveness of customs policies in increasing the resources devoted to controlling and inspection. Specifically, it seeks to analyze whether an increase in the administrative cost of collecting taxes on foreign trade in Ecuador contributes to reducing customs fraud. To this end, we identify and estimate a transfer function model (ARIMAX), considering information on foreign trade such as official international trade statistics report and tariff rates, as well as the execution of budgetary expenditure and Ecuador’s gross domestic product (GDP). The period under study includes quarterly series from 2006 to 2018. The results obtained by the model indicate that allocating greater material and budgetary resources to combat customs fraud does not always achieve the objective of reducing customs evasion.


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