Transpiration dynamics of an Austrian Pine stand and its forest floor: identifying controlling conditions using artificial neural networks

Towards understanding tree root profiles: simulating hydrologically optimal strategies for root distribution

“…Appelscha had higher values (small grey surface) in the upper layers and lower values in soil layers beneath a depth of 30 cm, the rooting depth of the undergrowth. As the under-storey was less sensitive to water stress than the trees (Vrugt et al, 2001), it is optimal to locate only a few roots in the upper soil layers, and any increase in the number of roots located there results in a relatively strong decrease in total water uptake. The Speuld root distribution showed a uniform sensitivity, whereas the Winterswijk configuration showed a high sensitivity in the lowest soil layers: a small deviation from the optimised amount of 0 m 3 m -3 roots resulted in a clear decrease in total transpiration.…”

“…1 and 3. For Appelscha, SWIF was adapted to include the evapotranspiration of the undergrowth, parameterised according to Vrugt et al (2001); this amounts to about 30% of the total forest ecosystem evapotranspiration in that site. One-third of the total calculated Makkink evapotranspiration was assigned to the undergrowth with a rooting depth of 0.25 m and the remainder was assigned to the trees of which the root distribution was optimised.…”

“…The coefficient 'a' of Eqn. (4) had a value of 0.43 for the tree-roots and a value of 0.67 for the undergrowth roots (Vrugt et al, 2001), thereby representing a smaller sensitivity to soil water stress of the undergrowth than that of the trees.…”

Water-oriented management in forest plantations: combining hydrology, dendrochronology and ecophysiology

“…In our case, a range of neural networks were tested varying the number of hidden nodes from i/2 to 2i, in order to select the optimal dimension of the network, appropriate to the complexity of the problem. In a similar way, a family of activation functions in the hidden and output layer were tested (logistic, hyperbolic tangent, and exponential for the hidden layer; exponential and identity function in the output node) (Vrugt et al 2002, Meijun et al 2007). …”

“…This is particularly important In recent years, ANN's have intensively been applied in forest and agriculture hydrology, e.g. estimating evapotranspiration (Kumar et al 2002, Kumar et al 2011, trunk sap flow ) and transpiration (Zee 2001a, Zee 2001b, Vrugt et al 2002, Garcia-Santos 2007, Meijun et al 2007). …”