Hydrologic Model for Drained Forest Watershed

McCarthy, E. J.; Flewelling, James W.; Skaggs, R. W.

doi:10.1061/(asce)0733-9437(1992)118:2(242)

Cited by 44 publications

(29 citation statements)

References 10 publications

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“…LAI has been well known to be a good integrator of many biological and physiological controls on ET processes (Chapin et al, 2004); thus, this finding was not surprising. LAI dynamics affect land surface albedo (Betts, 2000;Sun et al, 2010), stand canopy total conductance (Zeppel et al, 2008b;Ford et al, 2010), canopy interception rates (Helvey, 1967;McCarthy et al, 1992), root biomass and distribution (O'Grady et al, 2006), and the partitioning between evaporation and transpiration (Scott et al, 2006;Zhou et al, 2008). In fact, most process-based forest hydrological models (i.e.…”

Section: Relationships Between Et P Et O and Laimentioning

confidence: 99%

A general predictive model for estimating monthly ecosystem evapotranspiration

et al. 2010

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Accurately quantifying evapotranspiration (ET) is essential for modelling regional-scale ecosystem water balances. This study assembled an ET data set estimated from eddy flux and sapflow measurements for 13 ecosystems across a large climatic and management gradient from the United States, China, and Australia. Our objectives were to determine the relationships among monthly measured actual ET (ET), calculated FAO-56 grass reference ET (ET o ), measured precipitation (P), and leaf area index (LAI)-one associated key parameter of ecosystem structure. Results showed that the growing season ET from wet forests was generally higher than ET o while those from grasslands or woodlands in the arid and semi-arid regions were lower than ET o . Second, growing season ET was found to be converged to within š10% of P for most of the ecosystems examined. Therefore, our study suggested that soil water storage in the nongrowing season was important in influencing ET and water yield during the growing season. Lastly, monthly LAI, P, and ET o together explained about 85% of the variability of monthly ET. We concluded that the three variables LAI, P, and ET o , which were increasingly available from remote sensing products and weather station networks, could be used for estimating monthly regional ET dynamics with a reasonable accuracy. Such an empirical model has the potential to project the effects of climate and land management on water resources and carbon sequestration when integrated with ecosystem models.

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Section: Relationships Between Et P Et O and Laimentioning

confidence: 99%

A general predictive model for estimating monthly ecosystem evapotranspiration

et al. 2010

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“…The sapflow-based techniques to estimate ecosystem-level ET is limited to uniform stands that have few tree species with minor ET from understory plants (Wullschleger et al, 1998;Ewers et al, 2002;Ford et al, 2007). The most common practice to estimate short-term forest ET and runoff is employing the widely used Penman-Monteith equation (McCarthy et al, 1992) or empirical ET models driven by readily available meteorological variables (Amatya et al, 1995;Amatya and Skaggs, 2001;Lu et al, 2003;Harder et al, 2007;Sun et al, 2008b;Zhou et al, 2008;Amatya and Trettin, 2007). The eddy covariance method has gained popularity for simultaneously measuring both ET and CO 2 fluxes with high temporal scale due to performance improvements and reduced costs of fast-response monitoring sensors in recent years.…”

Section: Introductionmentioning

confidence: 99%

Energy and water balance of two contrasting loblolly pine plantations on the lower coastal plain of North Carolina, USA

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Noormets

Gavazzi

et al. 2010

Forest Ecology and Management

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“…DRAINWAT uses DRAINMOD and its forestry version DRAINLOB (McCarthy et al, 1992) modules for predicting daily field hydrology. Predicted total daily flows are routed to the field outlet using the SCS unit hydrograph method.…”

Section: Drainwatmentioning

confidence: 99%