Published in Soil Sci. Soc. Am. J. 69:492-499 (2005).
© 2005 Soil Science Society of America
677 S. Segoe Rd., Madison, WI 53711 USA
Division S-6—Soil & Water Management & Conservation
Evapotranspiration of Two Vegetation Covers in a Shallow Water Table Environment
Mahmood Nachabe*,
Nirjhar Shah,
Mark Ross and
Jeff Vomacka
Department of Civil and Environmental Engineering, University of South Florida, 4202 East Fowler Avenue, ENB 118, Tampa, FL 33620
* Corresponding author (nachabe{at}eng.usf.edu)
 |
ABSTRACT
|
|---|
A method is introduced to estimate evapotranspiration (ET) in shallow water table environments. The method involves measuring the diurnal fluctuations in total soil moisture above the water table to estimate (i) the net lateral and vertical subsurface flux in the aquifer and (ii) evapotranspiration from the vegetation cover. In a hillslope discharge zone, the net lateral subsurface flux was calculated from the recovery rate of soil moisture between midnight and 0400 h. Evapotranspiration was then estimated from a daily water balance in a soil column that included the water table. The method was tested on two vegetation covers, a pasture in a groundwater recharge area, and a riparian zone with woody vegetation in a groundwater discharge area. A moisture probe carrying eight sensors was used in each area to estimate the total soil moisture in a sandy soil environment. The observed water table fluctuated between land surface and a depth of 1.2 m during the study period, allowing observation and estimation of the total soil moisture in a soil column that included the water table. The results of this investigation support another hypothesis that, in humid, shallow water table environments, ET demand may be supported by adjacent ecosystems. This method provided reasonable results for the two landscapes investigated and was able to capture the variability of evapotranspiration in heterogeneous vegetation covers. It provided a relatively inexpensive alternative to characterize ET within regionally heterogeneous but microhomogenous landscapes. Though tested for coarse-textured soil, the method involving soil moisture monitoring can be easily adapted to other soil types with shallow water table. Another advantage of using this method is that ET can be successfully estimated without detailed knowledge of soil hydraulic properties, subsurface flow patterns, or vegetation characteristics.
Abbreviations: ET, evapotranspiration • TSM, total soil moisture
|
INTRODUCTION
|
|---|
IN MOST ENVIRONMENTS, evapotranspiration is the second-largest component of the hydrologic budget. In west-central Florida it has been estimated to be 70% of precipitation on an average annual basis (Bidlake et al., 1993; Knowles, 1996; Sumner, 2001). Despite its significance, ET is often treated as lumped residual flux from a catchment hydrologic budget, or estimated indirectly from an energy budget using information from a local weather station (e.g., Zhao, 1992; Singh, 1995). Recently, however, researchers have begun using scintillometers, remotely sensed data, and hydrologic models to resolve the spatial distribution of evapotranspiration (Kite and Droogers, 2000; Mo et al., 2004). Despite the progress in these methods, their applications in humid, shallow water table environments remain limited for a number of reasons. First, in these environments a highly heterogeneous vegetative cover creates a mosaic of intermingled ecosystems within short spatial scales. In west-central Florida, for example, distinct ecosystems like pasture, pine prairie, and wooded wetland may coexist naturally within 1 km2. Each vegetation type will have its distinct physiological characteristics like rooting depth, leaf area index, and stomatal resistance, which complicates the resolution of ET over the spatial domain.
In addition, the shallow water table in these humid environments allows water and nutrients to flow easily and weave adjacent ecosystems into a closely integrated landscape (Ewel, 1990). Indeed, small variation in depth to water table triggers localized groundwater flow that sustains the variability of ET over the spatial domain. Figure 1 shows the hydrograph for the water table in a groundwater discharge zone with a wooded vegetation cover. The water table, which dropped a net 12 cm during this 5-d period, exhibited significant diurnal fluctuations. It declined during daylight hours as a result of ET, and then partially recovered during night hours. Apparently, a hydraulic gradient developed between this forested area and the surrounding area as a result of the high ET for this vegetation cover. This hydraulic gradient initiated subsurface flow causing the water table to rise and further support the ET. Therefore, one-dimensional (vertical) hydrologic models that ignore the subsurface flow contribution may not adequately simulate ET in shallow water table environments (Droogers, 2000).
View larger version (18K):
[in this window]
[in a new window]
|
Fig. 1. Diurnal water table fluctuations in the groundwater discharge area, demonstrating partial water table recovery at night. NGVD, National Geodetic Vertical Datum.
|
|
Early observations of diurnal shallow water table fluctuation were made by White (1932), Troxell (1936), and Meyboom (1967). White (1932) proposed a method that utilizes observations of shallow water table fluctuation to estimate the direct consumption of groundwater by plants. However, the limitation of this method lies in the difficulty to estimate the specific yield: the volume of water released per unit water table fluctuation (Nachabe, 2002). Even lysimeters that are used for measuring water use by irrigated crops were found to give inaccurate estimates of ET when the state of groundwater was changing continuously with time (Yang et al., 2000). Naden et al. (2000) noted that hydrological models fall into two categories. The first category of models assumes independent spatial units with no internal variability or hydrologic linkage, while the second category allows for flow between units using a lateral soil moisture distribution function. Although the latter category of models supports lateral moisture redistribution, the spatial resolution is coarse, and may not resolve the spatial variation in ET for heterogeneous vegetation cover in shallow water table environments.
In this study, a methodology is introduced to estimate ET in shallow water table environments. The total soil moisture above and down through the water table is monitored continuously with time to capture the recovery or depletion of soil moisture. Subsurface flow seems to contribute significantly to the ET in discharge areas through maintaining the water table at a shallow depth. A number of studies have monitored soil moisture to estimate ET in the past. Fares and Alva (2000) concluded that continuous monitoring of moisture can be used to separate ET from deep water redistribution below the root zone of citrus. Robock et al. (2000) and Mahmood and Hubbard (2003), among others, have shown that accurate soil moisture monitoring can be successfully used to estimate ET from a hydrologic balance. Beyazgul et al. (2000) monitored soil moisture to capture the influence of the capillary fringe on the hydrologic budget of the soil. The method proposed in this article relies on the continuous monitoring of the total soil moisture in a soil column that included the water table. Subsurface fluxes between adjacent ecosystems are estimated from the diurnal fluctuation in total soil moisture. The main objectives of this study are to (i) introduce a methodology to estimate ET in shallow water table environments, (ii) test the methodology on different vegetation covers, and (iii) assess the contribution of lateral subsurface flow to ET in a discharge zone. Apart from being simple, easy to use, and relatively inexpensive, the method eliminates the error arising due to neglecting the capillary fringe, which can significantly affect estimated evapotranspiration (Beyazgul et al., 2000). This method seems suitable for shallow water table environments characterized by heterogeneous vegetation cover.
|
MATERIALS AND METHODS
|
|---|
The study site was in Hillsborough County, Florida, near the planned Tampa Bay Regional Reservoir in Lithia. The area is typical of central Florida with slopes ranging between 0 and 2% and highly permeable soils in the surface and subsurface layers (Carlisle et al., 1989). Marine sediments are the parent material for the Myakka fine sand (sandy, siliceous, hyperthermic Aeric Alaquods) at this site. The shallow water table fluctuated between land surface and a maximum depth of 1.2 m during this study, which lasted from May 2002 to September 2003. The vegetation cover varied from pasture grass in the upland, groundwater recharge area, to a mixture of oaks (Quercus spp.) and maple (Acer spp.) trees in the low-lying forested groundwater discharge area.
Monitoring the Total Soil Moisture
An EnviroSCAN soil moisture probe (Sentek, Adelaide, Australia) carrying eight soil moisture sensors was deployed in each area (see Fig. 2). The two probes, one in the pasture and a second in the forested area, were separated by a distance of 259 m. The surface elevation difference between the two locations was 3.34 m, providing an average ground slope of 1.3%. A PVC access tube was installed for each probe, and the soil moisture sensors were distributed at 10, 20, 30, 50, 70, 90, 110, and 150 cm below the land surface. These sensors work on the principle of electrical capacitance (frequency domain reflectometry) and are expected to provide volumetric water content ranging from oven dryness to saturation with a resolution of 0.1% (Buss, 1993). The default calibration equations provided by EnviroSCAN were used in this study. The principle of operation and accuracy of these sensors have been tested in both laboratory (Paltineanu and Starr, 1997) and field conditions (Starr and Paltineanu, 1998). Recently, Fares and Alva (2000) and Morgan et al. (1999) found no significant difference in the volumetric water content as measured by the capacitance sensors and the gravimetric method for fine sand in central Florida.
Data from the moisture sensors were used to estimate the total soil moisture (TSM) in the top 1.5 m of the soil profile. Mathematically, the total soil moisture is estimated as:
 | [1] |
where TSM is total soil moisture (m), z is depth (m) below land surface, and 
is the water content (m3 water/m3 soil). The sensors were programmed to record the water content at each depth every 5 min to allow continuous monitoring of the TSM with time. The trapezoidal rule of integration was used to approximate the TSM in Eq. [1]. Mathematically:
 | [2] |
where zi is the depth of the ith sensor from the land surface and i is water content at the ith sensor. Because no water content measurement was performed at the land surface where z = 0, a uniform water content is assumed in the top 10 cm of soil. With this approximation the expression for TSM becomes:
 | [3] |
where 1 is water content at z1 = 10 cm. In addition to the moisture sensors, a screened water table well was installed close to each soil moisture probe. The depth to water table was recorded every 5 min. The wells housed Instrumentation Northwest (Kirkland, WA) 0- to 5-psi (34-kPa) submersible pressure transducers, accurate to 0.005 psi (0.034 kPa). To prevent air compression inside the tube housing the transducer, the well was vented so the surface remained in direct connection with the atmosphere. A weather station was established adjacent to the moisture sensor in the pasture area. The weather station housed a standard Class A evaporation pan, a rainfall gauge, and instruments to measure radiation, temperature, humidity, and barometric pressure. The Class A evaporation pan has a 1210-mm diameter and a 255-mm depth. It is mounted on a wooden platform at a height of 150 mm above the ground to allow free circulation of air beneath the pan. The pan was made of galvanized iron and left unpainted following ASTM standards. Water level was measured at 5-min intervals using a pressure transducer (similar to the one used for the wells) attached to a data logger. The rainfall gauge is a tipping bucket type and records data at 5-min intervals.
Estimating Evapotranspiration from Change in Total Soil Moisture
In a shallow water table environment, the variation in total soil moisture on nonrainy days can be due to (i) subsurface flow from or to the 1.5-m soil column, and (ii) evapotranspiration from this soil column. Mathematically:
 | [4] |
where TSM is total soil moisture (see Eq. [1]), t is time (h), Q is subsurface flow rate (m/h), and ET is evapotranspiration rate (m/h). The negative sign in front of ET in Eq. [4] indicates that ET depletes the TSM in the column. The subsurface flow rate can be either positive or negative. In a groundwater discharge area, the subsurface flow rate, Q, is positive because it acts to replenish the TSM in the soil column. Obviously, this flow rate is negative in a groundwater recharge area. Figure 3 illustrates the role of subsurface flow in replenishing or depleting total soil moisture in the column.
View larger version (10K):
[in this window]
[in a new window]
|
Fig. 3. Total soil moisture is estimated in two soil columns. The first is in a groundwater recharge area (pasture), and the second is in a groundwater discharge area (forested). In a groundwater discharge area, subsurface flow acts to replenish the total soil moisture in the column, while this flux depletes the soil moisture in a groundwater recharge area.
|
|
To estimate both ET and Q in Eq. [4], it was important to decouple these fluxes. In this study, we estimated the subsurface flow rate from the diurnal fluctuation in TSM. Assuming ET is effectively zero between midnight and 0400 h, then Q can be easily calculated from Eq. [4] using:
 | [5] |
where TSM0400h and TSMmidnight are total soil moisture measured at 0400 h and midnight, respectively. The denominator in Eq. [5] is 4 h, corresponding to the time difference between the two TSM measurements. The assumption of negligible ET between midnight and 0400 h is not new, but was adopted in the early works of White (1932) and Meyboom (1967) in analyzing diurnal water table fluctuation. It is a reasonable assumption to make at night when sunlight is absent. Taking Q as constant for a 24-h period (White, 1932; Meyboom, 1967), the ET consumption in any single day was calculated from Eq. [4] as:
 | [6] |
where TSMj is the total soil moisture at midnight on day j, and TSMj+1 is the total soil moisture 24 h later (midnight the following day). Note that Q is multiplied by 24 h, because Eq. [6] provides daily ET values.
Equation [4] applies for dry periods, because it does not account for the contribution of interception storage to ET on rainy days. On rainy days, it was assumed that ET consumes interception storage in addition to any soil moisture. Interception storage was estimated to average 2.13 mm in the forested area and 1.39 mm for the pasture area. These values, which were estimated using rain gauges and soil moisture sensors at the site, seemed consistent with existing literature values for similar vegetation covers (e.g., Branson et al., 1981).
|
RESULTS AND DISCUSSION
|
|---|
Total Soil Moisture in Groundwater Recharge and Discharge Areas
The TSM evolution with time is shown in Fig. 4 and 5 for a period of 5 d in both areas. A noted distinction between the two graphs is the recovery of soil moisture at night in the groundwater discharge area (see Fig. 4). Essentially, the TSM in the soil column increased daily, from shortly before sunset (around 2000 h) until about 1000 h the following day. After this period, TSM declined rapidly because ET rates were larger than the rates of groundwater discharge to the soil column during the hot hours of the day. In contrast, in the uphill pastureland (Fig. 5), the TSM decreased continuously with time, due to both ET and drainage from the soil column. Obviously, during sunlight hours, the decline in TSM occurs at a faster rate as both fluxes are outward. The reduction in TSM during night hours is attributed to flow leaving the soil column and recharging the groundwater down gradient.
View larger version (18K):
[in this window]
[in a new window]
|
Fig. 4. Total soil moisture (TSM) versus time in the groundwater discharge area. The subsurface flux is the positive slope of the line between midnight and 0400 h.
|
|
View larger version (19K):
[in this window]
[in a new window]
|
Fig. 5. Total soil moisture (TSM) versus time in the groundwater recharge area. The slope of the line between midnight and 0400 h is negative and represents the subsurface flux drained from the soil column.
|
|
Estimated groundwater fluxes and ET for the two areas are presented in Table 1 for a period of 6 d. The negative sign of the subsurface flux for pasture in this table indicates that it is a loss from the soil column, and therefore it does not support ET in this area, rather it accentuates the loss in total soil moisture. In a groundwater discharge area, however, the positive groundwater flux to the soil column acts to support the high ET consumptive use. Results in Table 1 indicate that, during this period, groundwater discharge provided nearly 20% of the ET consumptive use for the wooded vegetation in the forest. This finding clearly demonstrates the dependence of the vegetation in groundwater discharge areas on subsurface flow contribution. Vertical one-dimensional hydrological models, which often ignore lateral groundwater contribution, will inappropriately estimate ET from the resultant hydrologic balance.
A positive correlation of 0.67 was observed between ET and the groundwater (discharge) flow into the forested area. This indicates that elevated ET might be partially responsible for increasing the discharge into the area. This should not be surprising, because the high consumptive use lowers the water table, which increases the hydraulic gradient between this area and its surrounding. This can be observed in Fig. 6, showing that the TSM and the water table elevation are synchronized with time. Indeed, a methodology was proposed by White (1932) to estimate ET from observations of water table fluctuation. However, the difficulty in this early approach lies in the estimation of the specific yield, a measure of water release per unit fluctuation in water table depth. Due to the capillary fringe, Nachabe (2002) suggested that specific yield of shallow water table environments cannot be constant but should vary with depth. Also, proper estimation of specific yield in shallow water table environments can be further complicated by hysteresis during wetting (water table rise) and drying (water table decline) cycles, and air encapsulation below the water table (Nachabe, 2002). The method used in this study relies on observation of total soil moisture, and thus does not require estimation of specific yield.
View larger version (24K):
[in this window]
[in a new window]
|
Fig. 6. Fluctuations in water table and total soil moisture for 5 d. The elevation of the land surface for this well is 23.21 m above National Geodetic Vertical Datum (NGVD).
|
|
Comparison with Pan Evaporation
To assess the robustness of the proposed method, the estimated ET values for pasture were compared with ET estimated from the evaporation pan. The measured pan evaporation was multiplied by a pan coefficient for pasture to estimate ET for this vegetation cover. A monthly variable crop coefficient was adopted (Doorenbos and Pruitt, 1977) to account for changes associated with seasonal plant phenology (see Table 2). The consumptive water use or the crop evapotranspiration is calculated as:
 | [7] |
where Ep is the measured pan evaporation, Kc is a pan coefficient for pastureland, and ETc is the estimated evapotranspiration (mm/d) by the pan evaporation method. Figure 7 compares the ET estimated by both the evaporation pan and moisture sensors for pasture. Although the two methods are fundamentally different, on average, estimated ET agreed well with an R2 coefficient of 0.78. This supported the validity of the soil moisture methodology, which further captured the daily variability of ET ranging from a low of 0.3 mm/d to a maximum of 4.9 mm/d. The differences between the two methods can be attributed to fundamental discrepancies that should be obvious. The pan results are based on atmospheric potential with crude average monthly coefficients while the TSM approach inherently incorporates plant physiology and actual moisture limitations. Indeed, both methods suffer from limitations. The pan coefficient is generic and does not account for regional variation in vegetation phenology or other local influences such as soil texture and fertility. Similarly, the accuracy of the soil moisture method proposed in this study depends on the number of sensors used in monitoring total moisture in the soil column.
View larger version (13K):
[in this window]
[in a new window]
|
Fig. 7. Evapotranspiration (ET) estimates for pasture by the pan and total soil moisture (TSM) methods. Data points represent the daily values of ET from both techniques.
|
|
Spatial and Temporal Variation in Evapotranspiration
Evapotranspiration is expected to vary both spatially and temporally. Spatial variability can be attributed to both differences in vegetation type, including variation in rooting depth, leaf area index, and stomatal resistance between pasture and wooded areas, or differences in soil properties, such as moisture and nutrient conditions. Soil moisture and nutrient characteristics often dictate the vegetation cover that can be supported by the soil. Figure 8 shows the resultant monthly averaged ET for the two vegetation covers studied. Clearly, ET in the forested area was consistently larger than ET for the pastureland by almost a factor of 2. The estimated annual ET for the forested area was 1320 mm, whereas the annual ET for pastureland was only 700 mm. The ET numbers for these vegetation covers were consistent with other evapotranspiration studies in Florida by the U.S. Geological Survey (Bidlake et al., 1993; Knowles, 1996; Sumner, 2001). This consistency was encouraging and enhanced the viability of the soil moisture method.
View larger version (18K):
[in this window]
[in a new window]
|
Fig. 8. Monthly average of evapotranspiration (ET) daily values in forested (diamonds) and pasture (triangles) areas. The gap in the graph represents a period of missing data. Standard deviations of daily values are also shown in the range limits.
|
|
In addition to spatial variability, the method seemed to capture well the temporal variability in ET (Fig. 8). In this study, temporal variability in ET existed at two time scales, a short-scale daily variation associated with daily changes in atmospheric conditions such as cloud cover, wind speed, temperature, and relative humidity, and a long-term, seasonal, climatic variation. The short-scale variation tends to be less systematic, and is demonstrated in Fig. 8 by range marks for the standard deviation of daily values for each month. The seasonal variation is more systematic and pronounced and is clearly captured by the method. As shown in Fig. 8, December and January were months with minimal ET, whereas May and June exhibited the highest values. This variation is attributed to longer daylight hours, more rainfall, and higher temperatures in the summer months as compared with the winter months in west-central Florida.
|
CONCLUSIONS
|
|---|
The objective of this study was to introduce and test a method for measuring vegetative ET. The advantage of this method over other commonly used methods is that just a simple measurement of soil moisture profile down through the root zone and below the water table is needed for the calculation of ET. Comparison of ET results for west-central Florida landscapes with values generated by simple pan data showed that the method behaves consistently and is robust. The method was also shown to capture both landscape spatial variations as well as short (daily) and long (seasonal) temporal variability. However, readers are cautioned that the absolute value of ET is not available; thus, nothing can be said conclusively about the superiority and the accuracy of the method. Also, the method is dependent on adequate vertical resolution of the moisture content and is limited by the depth of water table, which should always be shallower than the deepest moisture sensor; hence, the method is most easily applied to the shallow water table environments. Nevertheless, the results obtained are encouraging and provide questions and potential benefit for future study. The implications of the findings concerning hillslope subsurface flux for ET support are also significant. Urbanization and landform development in the upper hillslope contribute to reduced recharge and may ultimately affect downhill vegetative ET function and lower the water table, and otherwise impact alluvial wetlands.
|
ACKNOWLEDGMENTS
|
|---|
This work was made possible through funding from the USGS Florida Water Resources Research Center and the National Research Initiative, Competitive Grant Program of the USDA (CSREES Award 2001-35102-10829). M. Nachabe is the PI on both projects.
Received for publication May 27, 2004.
|
REFERENCES
|
|---|
- Beyazgul, M., Y. Kayan, and F. Engelsman. 2000. Estimation methods for crop water requirements in the Gediz Basin of western Turkey. J. Hydrol. (Amsterdam) 229:19–26.
- Bidlake, W.R., W.M. Woodham, and M.A. Lopez. 1993. Evapotranspiration from areas of native vegetation in west-central Florida. Water-Supply Paper 2430. USGS, Reston, VA.
- Branson, F.A., G.F. Gifford, K.G. Denard, and R.F. Hadley. 1981. Rangeland hydrology. 2nd ed. Kendall/Hunt, Dubuque, IA.
- Buss, P. 1993. The use of capacitance based measurements of real time soil water profile dynamics for irrigation scheduling. In Proc. Natl. Conf. Irrig. Assoc., Australia and Natl. Committee Irrig. Drain., Lauceston, Tasmania. 17–19 May 1993. Irrig. Assoc. of Australia, Homebush, NSW, Australia.
- Carlisle, V.W., F. Sodek, M. Collins, L.C. Hammond, and W.G. Harris. 1989. Characterization data for selected Florida soils. Inst. of Food and Agric. Sci., Univ. of Florida, Gainesville.
- Doorenbos, J., and W.O. Pruitt. 1977. Crop water requirements. FAO Irrigation and Drainage Paper 24. Food and Agric. Org. of the United Nations, Rome.
- Droogers, P. 2000. Estimating actual evapotranspiration using a detailed agro-hydrological model. J. Hydrol. (Amsterdam) 229:50–58.
- Ewel, K. 1990. Swamps. p. 281–323. In R.L. Myers and J.E. Ewel (ed.) Ecosystems of Florida. Univ. of Central Florida Press, Orlando.
- Fares, A., and A.K. Alva. 2000. Evaluating the capacitance probes for optimal irrigation of citrus through soil moisture monitoring in an Entisol profile. Irrig. Sci. 19:57–64.
- Kite, G.W., and P. Droogers. 2000. Comparing evapotranspiration estimates from satellites, hydrological models and field data. J. Hydrol. (Amsterdam) 229:3–18.
- Knowles, L., Jr. 1996. Estimation of evapotranspiration in the Rainbow Springs and Silver Springs basin in north-central Florida. Water Resour. Investigation Rep. 96-4024. USGS, Reston, VA.
- Mahmood, R., and K.G. Hubbard. 2003. Simulating sensitivity of soil moisture and evapotranspiration under heterogeneous soils and land uses. J. Hydrol. (Amsterdam) 280:72–90.
- Meyboom, P. 1967. Groundwater studies in the Assiniboine River drainage basin: II. Hydrologic characteristics of phreatophytic vegetation in south-central Saskatchewan. Geol. Survey of Canada Bull. 139(64). GSC, Ottawa.
- Mo, X., S. Liu, Z. Lin, and W. Zhao. 2004. Simulating temporal and spatial variation of evapotranspiration over the Lushi basin. J. Hydrol. (Amsterdam) 285:125–142.
- Morgan, K.T., L.R. Parsona, T.A. Wheaton, D.J. Pitts, and T.A. Oberza. 1999. Field calibration of a capacitance water content probe in fine sand soils. Soil Sci. Soc. Am. J. 63:987–989.[Abstract/Free Full Text]
- Nachabe, M.H. 2002. Analytical expressions for transient specific yield and shallow water table drainage. Water Resour. Res. 38(10), 1193, doi:10.1029/2001WR001071.
- Naden, P.S., E.M. Blyth, P. Broadhurst, C.D. Watts, and I.R. Wright. 2000. Modelling the spatial variation in soil moisture at the landscape scale: An application to five areas of ecological interest in the UK. Hydrol. Processes 14:785–809.
- Paltineanu, I.C., and J.L. Starr. 1997. Real-time soil water dynamics using multisensor capacitance probes: Laboratory calibration. Soil Sci. Soc. Am. J. 61:1576–1585.[Abstract/Free Full Text]
- Robock, A., K.Y. Vinnokov, G. Srinivasan, J.K. Entin, S. Hollinger, N.A. Spernskaya, S. Liu, and A. Namkhai. 2000. The global soil moisture data bank. Bull. Am. Meteorol. Soc. 81:1281–1299.
- Singh, V.P. 1995. Computer model of watershed hydrology. Water Resour. Publ., Highlands Ranch, CO.
- Starr, J.L., and I.C. Paltineanu. 1998. Soil water dynamics using multisensor capacitance probes in nontraffic interrows of corn. Soil Sci. Soc. Am. J. 62:115–122.
- Sumner, D.M. 2001. Evapotranspiration from a cypress and pine forest subjected to natural fires, Volusia County, Florida, 1998-99. Water Resour. Investigations Rep. 01-4245. USGS, Reston, VA.
- Troxell, H.C. 1936. The diurnal fluctuation in the ground-water and flow of the Santa Ana River and its meaning. Trans. Am. Geophys. Union 17:496–504.
- White, W.N. 1932. A method of estimating ground-water supplies based on discharge by plants and evaporation from soil: Results of investigation in Escalante Valley, Utah. Water-Supply Paper 659-A. USGS, Reston, VA.
- Yang, J., B. Li, and S. Liu. 2000. A large weighing lysimeter for evapotranspiration and soil water-groundwater exchange studies. Hydrol. Processes 14:1887–1897.
- Zhao, R.J. 1992. The Xinanjiang model applied in China. J. Hydrol. (Amsterdam) 135:371–381.
This article has been cited by other articles:
|
|
|
|
 
S. L. Chinault and G. A. O'Connor
Phosphorus Release from a Biosolids-Amended Sandy Spodosol
J. Environ. Qual.,
May 1, 2008;
37(3):
937 - 943.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
W.-L. Liang, K. Kosugi, and T. Mizuyama
Heterogeneous Soil Water Dynamics around a Tree Growing on a Steep Hillslope
Vadose Zone J.,
November 20, 2007;
6(4):
879 - 889.
[Abstract]
[Full Text]
[PDF]
|
|
|
TOP
ABSTRACT
INTRODUCTION
