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Coupling daily transpiration modelling with forest management in a semiarid pine plantation

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Coupling daily transpiration modelling with forest management in a semiarid pine plantation

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dc.contributor.author Gualberto-Fernandes, Tarcisio Jose es_ES
dc.contributor.author Campo García, Antonio Dámaso del es_ES
dc.contributor.author García Bartual, Rafael Luis es_ES
dc.contributor.author GONZÁLEZ-SANCHIS, MARÍA DEL CARMEN es_ES
dc.coverage.spatial east=-1.192757400000005; north=39.06144539999999; name=46620 Aiora, València, Espanya es_ES
dc.date.accessioned 2017-04-27T07:19:06Z
dc.date.available 2017-04-27T07:19:06Z
dc.date.issued 2016-02
dc.identifier.issn 1971-7458
dc.identifier.uri http://hdl.handle.net/10251/80086
dc.description.abstract [EN] Estimating forest transpiration is of great importance for Adaptive Forest Management (AFM) in the scope of climate change prediction. AFM in the Mediterranean region usually generates a mosaic of different canopy covers within the same forest. Several models and methods are available to estimate forest transpiration, but most require a homogeneous forest cover, or an individual calibration/validation process for each cover stand. Hence, a model capable of reproducing accurately the transpiration of the whole canopy-cover mosaic is necessary. In this paper, the use of Artificial Neural Network (ANN) is proposed as a flexible tool for estimating forest transpiration using the forest cover as an input variable. To that end, sap flow, soil water content and other environmental variables were experimentally collected under five Aleppo pine stands of different canopy covers for two years. These sets of inputs were then used for the ANN training. Stand transpiration was accurately estimated using climate data, soil water content and forest cover through the ANN approach (correlation coefficient R = 0.95; Nash-Sutcliffe coefficient E = 0.90; root-meansquare error RMSE = 0.078 mm day(-1)). Finally, the input value for soil water content (when not available) was computed using the process-based model Gotilwa+. Then, this computed soil water content was used as input in the proposed ANN. This combination predicted the forest transpiration with values of R = 0.90, E = 0.63, and RMSE = 0.068 mm day(-1). Artificial Neural Network proved to be a useful and flexible tool to predict the transpiration dynamics of an Aleppo pine stand regardless of the heterogeneity of the forest cover produced by adaptive forest management. es_ES
dc.description.sponsorship The authors are grateful to the Valencia Regional Government (CMAAUV, Generalitat Valenciana) and the VAERSA staff for allowing the use of the La Hunde experimental forest and for assistance in carrying out the fieldwork. The authors thank Rafael Herrera and Lelys Bravo de Guenni for their critical reviews of earlier versions of this manuscript. The authors thank Sabina Cerruto Ribeiro for improving the English language. The authors thank the reviewers for their contributions to improve the scientific quality of this paper. TJGF thanks the Mundus 17 program, coordinated by the University of Porto, Portugal. This study is a part of research projects: "CGL2011-28776-C02-02, HYDROSIL", "CGL 2014-58127-C3-2, SILWAMED," funded by the Spanish Ministry of Science and Innovation and FEDER funds, and "Determination of hydrologic and forest recovery factors in Mediterranean forests and their social perception", led by Dr. Eduardo Rojas and supported by the Ministry of Environment, Rural and Marine Affairs (Spanish Government). en_EN
dc.language Inglés es_ES
dc.relation Ministerio de Ciencia e Innovación, Spain [CGL2011-28776-C02-02] [CGL2014-58127-C3-2] es_ES
dc.relation.ispartof iForest: biogeosciences and forestry es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Adaptative forest management es_ES
dc.subject Artificial neural network (ANN) es_ES
dc.subject Forest water use es_ES
dc.subject Pinus halepensis Mill es_ES
dc.subject.classification INGENIERIA HIDRAULICA es_ES
dc.subject.classification TECNOLOGIA DEL MEDIO AMBIENTE es_ES
dc.title Coupling daily transpiration modelling with forest management in a semiarid pine plantation es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3832/ifor1290-008
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos - Escola Tècnica Superior d'Enginyers de Camins, Canals i Ports es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Ingeniería del Agua y del Medio Ambiente - Institut Universitari d'Enginyeria de l'Aigua i Medi Ambient es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingeniería Agronómica y del Medio Natural - Escola Tècnica Superior d'Enginyeria Agronòmica i del Medi Natural es_ES
dc.description.bibliographicCitation Gualberto-Fernandes, TJ.; Campo García, ADD.; García Bartual, RL.; González-Sanchis, MDC. (2016). Coupling daily transpiration modelling with forest management in a semiarid pine plantation. iForest: biogeosciences and forestry. 9:38-48. doi:10.3832/ifor1290-008 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://doi.org/10.3832/ifor1290-008 es_ES
dc.description.upvformatpinicio 38 es_ES
dc.description.upvformatpfin 48 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 9 es_ES
dc.relation.senia 306584 es_ES


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