Calibration of a Two-Dimensional Root Water Uptake Model

mates of the temporal and spatial root water uptake patterns are needed. Quantification of root water exAlthough solutions of multidimensional transient water flow can traction rates also contributes to an improved underbe obtained by numerical modeling, their application may be limited in part as root water uptake is generally considered to be one-dimenstanding of chemical fluxes in the vadose zone in both sional only. The objective of this study was to develop and test a twoecological and hydrological studies (Somma et al., 1998), dimensional root water uptake model, which can be incorporated as well as their control by vegetation. Water uptake by into numerical multidimensional flow models. The two-dimensional rooting systems can control the timing and the amount uptake model is based on the model by Raats, but is extended with of chemical pollutant loadings to the groundwater a radial component. Subsequently, the root water uptake model was through elimination of preferential flow patterns of waincorporated into a two-dimensional flow model, and root water upter and chemicals, or by regulation of absorption of take parameters were optimized, minimizing the residuals between nutrients or trace elements, thereby reducing their conmeasured and simulated water content data. Water content was meacentration levels in the deep vadose zone or groundwasured around a sprinkler-irrigated almond tree (Prunus laurocerasus ter (Clothier and Green, 1994). Moreover, the rhizoM.J.Roem) for a 16-d period at 25 locations, following irrigation. To calibrate the flow and root water uptake model, a genetic algorithm sphere might be responsible for accelerated breakdown (GA) was used to find the approximate global minimum of the optiof organic chemicals by biodegradation (Walton and mized parameter space. The final fitting parameters were determined Anderson, 1990). using the Simplex algorithm (SA). With the optimized root water Actual root water uptake not only depends on the uptake parameters, simulated and measured water contents during root distribution and its functioning, but also on soil the 16-d period were in excellent agreement, with R2 values generally water availability and salinity. In addition to water stress ranging between 0.94 and 0.99 and a root mean squared error (RMSE) in periods of low water availability, root water uptake of 0.015 m3 m 3. The developed root water uptake model is extremely is also reduced when concentrations of soluble salts flexible and allows spatial variations of water uptake as influenced by exceed plant-specific threshold values (Homaee, 1999). nonuniform (drip irrigation) and uniform water application patterns. In irrigated soils, particularly in arid and semiarid regions, plants are generally subjected to both salinity and water stress. In these regions, soil and water manageF a hydrological perspective, water uptake by ment practices are based on maintaining a favorable root systems and their spatial distribution can largely soil water content and salinity status in the root zone, control water fluxes to the atmosphere and the groundthereby minimizing periods of water stress while conwater (Canadell et al., 1996). For an improved undertrolling leaching to minimize salinity stress. standing of the magnitude of these fluxes, accurate estiThe influence of plant–root systems on water and chemical movement can be better understood using soil J.A. Vrugt, Institute for Biodiversity and Ecosystem Dynamics, Uniwater simulation models, provided that accurate spatial versity of Amsterdam, The Netherlands, Nieuwe Prinsengracht 130, and temporal root water uptake distributions are inAmsterdam, 1018 VZ; J.W. Hopmans, Hydrology Program, Dept. cluded (Musters and Bouten, 1999; Musters, 1998). The Land, Air and Water Resources (LAWR), University of California, Davis, CA 95616, USA; J. Šimunek, USDA Salinity Laboratory, University of California, Riverside, CA 95207, USA. Received 18 Nov. Abbreviations: CIMIS, California Irrigation Management Informa1999 *Corresponding author ([email protected]). tion System; CV, coefficient of variation; GA, genetic algorithm; RMSE, root mean squared error; SA, simplex algorithm. Published in Soil Sci. Soc. Am. J. 65:1027–1037 (2001).