dot
Detailansicht
Katalogkarte GBA
Katalogkarte ISBD
Suche präzisieren
Drucken
Download RIS
Hier klicken, um den Treffer aus der Auswahl zu entfernen
Titel Comparison of different tree sap flow up-scaling procedures using Monte-Carlo simulations
VerfasserIn Fyodor Tatarinov, Yakir Preisler, Shani Roahtyn, Dan Yakir
Konferenz EGU General Assembly 2015
Medientyp Artikel
Sprache Englisch
Digitales Dokument PDF
Erschienen In: GRA - Volume 17 (2015)
Datensatznummer 250109258
Publikation (Nr.) Volltext-Dokument vorhandenEGU/EGU2015-9151.pdf
 
Zusammenfassung
An important task in determining forest ecosystem water balance is the estimation of stand transpiration, allowing separating evapotranspiration into transpiration and soil evaporation. This can be based on up-scaling measurements of sap flow in representative trees (SF), which can be done by different mathematical algorithms. The aim of the present study was to evaluate the error associated with different up-scaling algorithms under different conditions. Other types of errors (such as, measurement error, within tree SF variability, choice of sample plot etc.) were not considered here. A set of simulation experiments using Monte-Carlo technique was carried out and three up-scaling procedures were tested. (1) Multiplying mean stand sap flux density based on unit sapwood cross-section area (SFD) by total sapwood area (Klein et al, 2014); (2) deriving of linear dependence of tree sap flow on tree DBH and calculating SFstand using predicted SF by DBH classes and stand DBH distribution (Cermak et al., 2004); (3) same as method 2 but using non-linear dependency. Simulations were performed under different SFD(DBH) slope (bs, positive, negative, zero); different DBH and SFD standard deviations (δd and δs, respectively) and DBH class size. It was assumed that all trees in a unit area are measured and the total SF of all trees in the experimental plot was taken as the reference SFstand value. Under negative bs all models tend to overestimate SFstand and the error increases exponentially with decreasing bs. Under bs >0 all models tend to underestimate SFstand, but the error is much smaller than for bs