Transpiration reduction functions are often used in hydrological modeling to estimate actual transpiration as a function of soil water status. Empirical reduction functions are most frequently used due to the higher data needs and computational requirements of mechanistic models. Empirical models, however, lack a description of physical mechanisms and their parameters require extensive calibration. We derive a process‐based reduction function predicting system potentials, resistances, and water flows. An analytical solution for a special case of Brooks and Corey soils is presented. A numerical version of the reduction function for van Genuchten soils was implemented in the Soil–Water–Atmosphere–Plant (SWAP) hydrological model, allowing predictions for layered soil profiles and root length density variations over depth. The analytical and numerical versions of the model allow an increasingly quantitative insight into the mechanism of root water uptake, such as the existence of a maximum root water uptake rate as a function of soil water status, soil hydraulic properties, root length density, and root radius, in addition to the fact that sensitivity of simulated root water uptake to the radial root conductivity and axial conductance decrease when root length density increases. The approach can be used for the estimation of threshold values for empirical reduction functions.
Abstract. Detailed physical models describing root water uptake (RWU) are an important tool for the prediction of RWU and crop transpiration, but the hydraulic parameters involved are hardly ever available, making them less attractive for many studies. Empirical models are more readily used because of their simplicity and the associated lower data requirements. The purpose of this study is to evaluate the capability of some empirical models to mimic the RWU distribution under varying environmental conditions predicted from numerical simulations with a detailed physical model. A review of some empirical models used as sub-models in ecohydrological models is presented, and alternative empirical RWU models are proposed. All these empirical models are analogous to the standard Feddes model, but differ in how RWU is partitioned over depth or how the transpiration reduction function is defined. The parameters of the empirical models are determined by inverse modelling of simulated depth-dependent RWU. The performance of the empirical models and their optimized empirical parameters depends on the scenario. The standard empirical Feddes model only performs well in scenarios with low root length density R, i.e. for scenarios with low RWU "compensation". For medium and high R, the Feddes RWU model cannot mimic properly the root uptake dynamics as predicted by the physical model. The Jarvis RWU model in combination with the Feddes reduction function (JMf) only provides good predictions for low and medium R scenarios. For high R, it cannot mimic the uptake patterns predicted by the physical model. Incorporating a newly proposed reduction function into the Jarvis model improved RWU predictions. Regarding the ability of the models to predict plant transpiration, all models accounting for compensation show good performance. The Akaike information criterion (AIC) indicates that the Jarvis (2010) model (JMII), with no empirical parameters to be estimated, is the "best model". The proposed models are better in predicting RWU patterns similar to the physical model. The statistical indices point to them as the best alternatives for mimicking RWU predictions of the physical model.
, respectively. The H 2 O 2 concentrations showed a positive and significant correlation with HCOO -(r = 0.70, n = 129, p < 0.0001) suggesting that the production of H 2 O 2 in aqueous phase by consumption of formaldehyde by reaction with OH • radical is a factor which may control the H 2 O 2 levels in the rainwater samples. Estimation of rate of wet deposition of H 2 O 2 shows that nearly 70% of hydrogen peroxide is annually removed from the atmosphere by wet deposition in spring and summer. Sequential rainstorm analyses indicated that lightning activity and rainfall rate can influence the H 2 O 2 contents in rainwater in this area.
R ESU M OO crescimento da cana-de-açúcar pode ser obtido por modelos biofísicos em que a fotossíntese bruta (FB) é obtida em função da radiação solar. O objetivo do trabalho é avaliar variedades de canas-deaçúcar em regime irrigado em relação à radiação fotossinteticamente ativa interceptada (RFA INT ) e a estimativa da FB acumulada. Para isto, conduziu-se um estudo na Universidade Federal de Alagoas, entre 2008 e 2009, com variedades de cana RB. Foram realizadas medidas biométricas, variáveis de produção e dos elementos meteorológicos. A irradiância fotossintética (RFA) interceptada foi obtida pela diferença entre RFA e RFA transmitida (RFA T ). A RFA T foi determinada pela Lei de Beer. Na estimativa da FB diária usou-se uma integração numérica, com uma abordagem trapezoidal. As variáveis de produção tiveram correlações com a RFA INT acumulada e com a FB acumulada durante o ciclo. A média da irradiação solar global diária do período chuvoso da região (maio -agosto) foi igual a 14,9 MJ m -2. A variedade RB92579 teve os maiores variáveis de produção, como também maiores RFA interceptada e FB acumuladas no ciclo, devido à sua maior capacidade de rebrotação e conversão de energia em fotoassimilados.Palavras-chave: radiação fotossintética, modelos de crescimento, Saccharum spp. Growth and photosynthesis of sugarcane based on biometric and meteorological variablesA B ST R A C T Sugarcane growth can be obtained by biophysical models in which gross photosynthesis (GP) is obtained as a function of solar radiation. This work aims to evaluate sugarcane varieties under irrigation in relation to intercepted photosynthetic active radiation (PARint) and the estimated accumulative GP. To achieve that, a study was conducted at the Federal University of Alagoas during 2008 and 2009, with RB sugarcane varieties. Biometric measurements, production variables and meteorological elements were made. The intercepted photosynthetic irradiance (PAR) was obtained by the difference between PAR and transmitted PAR (PAR T ), which was determined by Beer's Law. The daily GP was estimated numerically by the trapezoidal approach. The production variables had correlations with accumulated PARint and accumulated GP during the crop cycle. The average global solar radiation in the region for rainy season (May-August) was 14.9 MJ m -2 . The variety RB92579 had the highest production variables as well as higher intercepted PAR and accumulated GP in the cycle due to its greater capacity for regrowth and energy conversion in photoassimilate.
RESUMOO objetivo do presente trabalho foi estabelecer o modelo digital de elevação e sua resolução horizontal para interpolar a temperatura do ar anual para o estado de Alagoas via modelos de regressão linear múltipla. Ajustou-se um modelo de regressão linear múltipla a séries (11 a 34 anos) de temperatura do ar anual de 28 estações meteorológicas dos estados de Alagoas, Bahia, Pernambuco e Sergipe, em função da latitude, longitude e altitude. Os modelos de elevação considerados nas análises foram o SRTM e o GTOPO30, com resoluções originais de 90 e 900 m, respectivamente. O SRTM foi reamostrado para as resoluções de 125, 250, 500, 750 e 900 m. Na espacialização da temperatura do ar para Alagoas, utilizou-se da regressão linear múltipla aplicada a cada modelo de elevação e resolução espacial e a um grid com a latitude e longitude. Para Alagoas, as estimativas baseadas no SRTM mostraram erro padrão de estimativa (0,57 ºC) e dispersão (r 2 = 0,62) inferiores às obtidas pelo GTOPO30 (0,93 ºC e 0,20). No caso das resoluções do SRTM, não se observaram diferenças significativas entre o erro padrão (0,55 ºC; 750 m-0,58 ºC; 250m) e a dispersão (entre 0,60 -500 m e 0,65 -750 m) das estimativas. A espacialização da temperatura do ar anual para Alagoas via modelos de regressão múltipla aplicados ao SRTM mostra concordância superior à obtida com o GTOPO30, independente da resolução espacial. Palavras-chave: GTOPO30; SRTM; modelos geoestatísticos. ABSTRACTThe aim of this study was to establish a digital elevation model and its horizontal resolution to interpolate the annual air temperature for the Alagoas State by means of multiple linear regression models. A multiple linear regression model was adjusted to series (11 to 34 years) of annual air temperatures obtained from 28 weather stations in the states of Alagoas, Bahia, Pernambuco and Sergipe, in the Northeast of Brazil, in function of latitude, longitude and altitude. The elevation models SRTM and GTOPO30 were used in the analysis, with original resolutions of 90 and 900 m, respectively. The SRTM was resampled for horizontal resolutions of 125, 250, 500, 750 and 900 m. For spatializing the annual mean air temperature for the state of Alagoas, a multiple linear regression model was used for each elevation and spatial resolution on a grid of the latitude and longitude. In Alagoas, estimates based on SRTM data resulted in a standard error of estimate (0.57 º C) and dispersion (r 2 = 0.62) lower than those obtained from GTOPO30 (0.93ºC and 0.20).
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