Vegetation exerts a strong control over the hydrological cycle, including groundwater recharge, which provides water for many human and natural communities. Understanding the effect of vegetation on recharge globally within the relevant physical constraints such as climate and soil will help land-use decisions for sustainable groundwater management.

Because groundwater is an essential resource for people and ecosystems, a better understanding is needed of the fundamental controls on recharge and its interactions with vegetation change. We analyzed >600 estimates of groundwater recharge to obtain the first global analysis of recharge and vegetation types. Globally, croplands had the highest proportion of water input (WI = precipitation + irrigation) that become recharge, followed by grasslands, woodlands, and scrublands (average proportional recharge: 0.11, 0.08, 0.06, and 0.05, respectively; P < 0.0001). A stepwise regression model revealed that WI had the strongest association with recharge overall, followed by vegetation type, potential evapotranspiration (PET), saturated hydraulic conductivity based on soil texture (Ks), and seasonality of rainfall (R2 = 0.29, 0.16, 0.12, 0.06, and 0.01, respectively; P < 0.0001). Recharge increased with increasing WI, Ks, and seasonality of rainfall and decreased with increasing PET. Relative differences in recharge among vegetation types were larger in drier climates and clayey soils, indicating greater biological control on soil water fluxes under these conditions. To further test the relationship between recharge and vegetation, we compared global synthesis data to our parallel field estimates of recharge in paired grasslands, croplands, and woodlands across the Argentinean Pampas and the southwestern United States. Our field estimates of recharge were similar to, and followed the same pattern of, recharge under vegetation types in the synthesis data, suggesting that land-use changes will continue to alter recharge dynamics and vadose zone processes globally. The results of this study highlight the implications of land-use management for sustainable groundwater use and should also help test and improve recharge estimates in large-scale water balance and climate models.

Groundwater sustains the lives of one quarter of the human population (Ford and Williams, 1989; White et al., 1995) and is vital for industrial, agricultural, and recreational activities and for the health of other species and ecosystems (Postel and Carpenter, 1997; Jackson et al., 2001). Its importance is most apparent in arid and semiarid regions, where a paucity of surface waters often leads to greater groundwater exploitation. Given the increasing use and scarcity of groundwater in many locations, and its relatively slow replenishment, sustainable groundwater use and management are necessary to meet the needs of people and ecosystems (Shiklomanov, 1997; Shah et al., 2000; Vörösmarty et al., 2000).

The relationships between groundwater recharge and physical variables have long been of scientific and practical interest, traceable back to ancient Roman times (Dr. Nitish Priyadarshi, earthday.ning.com/profiles/blogs/1734264:BlogPost:24384 [verified 23 Oct. 2011]). Previous studies have identified climatic and geologic factors as major environmental controls on the rate of groundwater recharge. In general, recharge increases with the amount and intensity of rainfall, which influence how much water enters the soil and rocks (Lvovitch, 1970; Freeze and Cherry, 1979; Bredenkamp, 1988; Edmunds, 2001a; Jan et al., 2007; Stonestrom et al., 2007). In contrast, recharge typically decreases with increasing PET, an expression of the amount of energy available to evaporate water (Thornthwaite et al., 1957). Once in the soil, the movement of water is influenced by soil texture and structure, with sandier soils tending to have greater rates of recharge and more clayey soils having increased tortuosity and more limited water movement (Athavale et al., 1980; Kennett-Smith et al., 1994). Such general relationships are already important for use in some global models (e.g., Döll et al., 2003).

One aspect of recharge that is less well understood and rarely incorporated into global land-surface models is the effect of vegetation on recharge (Jackson et al., 2000; Gerten et al., 2004). Examples of key uncertainties include the primary effect of vegetation type compared with the physical climate and soil variables, as well as how changes in vegetation interact with climate and soils to alter recharge. Although considerable research has examined physical factors as controls of recharge, earlier work has rarely emphasized the effects of vegetation (but see Lull and Munns [1950] and also Petheram et al. [2002] for a review of Australian studies). Several studies have included vegetation in attempts to model groundwater recharge at various scales (i.e., Finch, 1998; Keese et al., 2005; Döll and Fiedler, 2008), although most have found or assumed the relationship to be of secondary importance compared with the effects of physical factors such as climate and soil on recharge.

Plants often mediate water fluxes between the soil and the atmosphere through the uptake of soil water by roots and through evapotranspiration (ET) from leaves, with plant traits such as rooting depth, leaf area, and phenology affecting the magnitude and duration of these fluxes (Skiles and Hanson, 1994; Neilson, 1995; Milly, 1997; Kergoat, 1998; Peel et al., 2001; Jackson et al., 2008). Pervasive land-use and land-cover changes from anthropogenic and natural forces could have large consequences for groundwater recharge and potentially for downstream effects such as salinization (Walker et al., 1999). Building on earlier studies of land use and recharge, including studies in Australia (Petheram et al., 2002) and in arid and semiarid regions (Scanlon et al., 2006), we examined the relative importance of vegetation in the relationship between recharge and physical factors.

We compiled a new global synthesis of groundwater recharge rates and data for different climates, soils, and vegetation types to understand how different vegetation types affect recharge. We hypothesized that vegetation would exert as strong an influence on recharge as climate and soils do. Moreover, we expected strong interactions among plant, climate, and soil factors that would create predictable patterns of recharge under different vegetation types. Among vegetation types, we emphasized croplands, grasslands, and forests because shifts among these common land covers represent most of the ongoing land-use changes today (Meyer and Turner, 1994; Klein Goldewijk and Battjes, 1997). To test the synthesis data and to compare recharge under paired vegetation types, we also collected new field data from paired land uses across precipitation gradients in central Argentina and the southwestern United States. We applied our findings to examine how land-use and land-cover changes may affect recharge across climatic and soil factors.

A conceptual model of recharge suggests several important soil and climate factors that affect recharge:
graphic
where R is recharge (mm yr−1), P is precipitation and irrigation (mm yr−1), dS/dt is any change in soil moisture storage (mm yr−1), α is the proportion of P that becomes throughfall, and ET is an evapotranspiration term that is a function of soil water availability and the energy available for evaporation (mm yr−1). Vegetation is likely to affect α through the interception of rainfall by leaves and branches and to affect ET through such factors as the coupling of vegetation to the atmosphere (e.g., through more leaves or taller vegetation stature) and to soil moisture (e.g., through deeper roots). The studies that we reviewed globally and our specific study sites were located in relatively level landscapes to minimize the effect of runoff, which is not considered in this conceptual framework.
Because available soil moisture can become either ET or R, the potential rate at which water moves through the soil matrix, and therefore out of the zone of root uptake, is another important determinant of recharge. When there is uniform matric potential, recharge is affected largely by the gravitational gradient and represented by simplified Darcy's law (Clapp and Horneberger, 1978):
graphic
where Ks is the saturated hydraulic conductivity, θ is the soil moisture below the root zone (dependent on evaporation from the soil surface, root water uptake, and downward flux of water out of the root zone as determined by the water potential gradient), θs is soil moisture content at saturation, and b is an empirical parameter that varies with soil texture.

Because θ, θs, and Ks are not always reported in published studies, we estimated Ks in our regression model of recharge based on the soil texture information that the studies provided more frequently (see below). Furthermore, because ET depends on the available soil moisture, we used the energy available for evaporation or PET as a proxy for ET. Although our approach was statistical, we chose the predictors for the regression model based on this conceptual framework. The seasonal amplitude of rainfall and synchrony of rainfall with PET are both additional important considerations in the water balance because these factors affect the downward soil water flux bypassing root uptake (Milly, 1994; Potter et al., 2005). The predictors we chose for our model were precipitation, PET, Ks, and the seasonality of rainfall in addition to vegetation type.

We examined studies of recharge and physical variables associated with land use or vegetation type, identified using literature searches involving the keywords “groundwater recharge,” “deep drainage,” or “residual flux,” henceforth collectively referred to as recharge (Petheram et al., 2002; Scanlon et al., 2006). From tables, digitized graphs, and text, we recorded recharge estimates, precipitation during the study period or the reported long-term mean (P), PET, soil texture (clay and sand contents or textural classes), Ks, vegetation type, species present, and the amount of irrigation (I), when present. In studies where recharge estimates included data from multiple years and locations, such as those using permanent boreholes for the same vegetation and soil type, we used the mean of the estimates. Across the data set, 46% of the data points came from Oceania, 19% from North America, 15% from Asia, 10% from Africa, 6% from Europe, and 3% from South America (see Fig. 1).

Fig. 1.

Locations of the study sites included in the global synthesis.

Fig. 1.

Locations of the study sites included in the global synthesis.

Because we wanted to compare the effects of biological and physical variables on recharge, we excluded data from sites with significant sources or sinks for runoff, such as sinkholes, playas, and streams. Studies that estimated recharge for <1 yr were also excluded from our analysis. Due to the large number (>2500) of studies in the search, we sorted the results by relevance in the Web of Knowledge (Thomson Reuters, Philadelphia, PA) and included studies until fewer than two out of 10 additional studies yielded data on the following variables: recharge, vegetation type, precipitation, irrigation, and soil texture or Ks.

For the vegetation analyses, we divided plant types into five broad categories: cropland, grassland, woodland, scrubland, and no vegetation. Annual agricultural fields were classified as croplands; grasslands and pastures as grasslands; forests and woodlands as woodlands; scrublands, heathlands, shrublands, steppes, fynbos, and savannas as scrublands; and areas with sparse or no vegetation as “NoVeg.”

Most studies did not provide data for PET or the seasonality of rainfall, and these variables were therefore obtained from the high spatial resolution (10′ by 10′) Climate Research Unit global data set (New et al., 2002; csi.cgiar.org/cru/index.asp [verified 24 Oct. 2011]), using locations of sites given in the studies. We calculated PET using the Penman–Monteith equation from the monthly climate data set. We defined two variables associated with the seasonality of rainfall (Milly, 1994; Potter et al., 2005): (i) the difference between the maximum and minimum mean monthly rainfall (amplitude); and (ii) the number of months between the maximum mean monthly temperature and precipitation (phase). Water input, the aridity index (AI), and potential water excess (PWE) were calculated as P + I, (P + I)/PET, and P + I − PET, respectively, to identify the climatic index with the strongest associations with recharge.

We estimated Ks using soil texture classes (Rawls et al., 1982). Where different soil horizons existed within the depth of soil examined, the estimated Ks for the top layer was used. To ensure that our estimates of PET and Ks were reasonable, we compared them with values of PET and Ks from the subset of studies where they were reported. Our estimates matched well with reported the PET and Ks values across the studies [n = 220, 71; R2 = 0.71, 0.70; P < 0.0001, 0.0001, for PET and log(Ks), respectively].

Proportional recharge (P/WI) between each pair of vegetation and soil types was compared using a Kruskal–Wallis test. Proportional recharge was used for this analysis instead of recharge because it allowed comparisons after controlling for the effect of WI. A nonparametric test was used because the data were not normally distributed. Grouping the data into two soil texture categories was done for some analyses to more easily examine the effects of soil texture on recharge: “clays” were defined as soils whose estimated Ks was <0.25 m d−1 (silt loam and more clayey soils) and “sands” were texture classes with higher values of Ks.

We tested all climate variables (WI, AI, PET, PWE, and seasonality) and models (linear, logarithmic, exponential, and sigmoidal) to determine the best predictor of recharge. This approach was taken to choose a single best predictor variable to easily represent and compare the synthesis data with the new field data (see below). Due to the relatively low sample size (n < 50) and limited ranges in climatic variables (e.g., WI = 159–937 mm yr−1) for scrublands and NoVeg, those two vegetation types were excluded from the curve-fitting and regression analyses (see below). All of the models tested were susceptible to the influence of relatively few data points at the most humid end of our data range (n = 5 for the perhumid region data); we therefore limited our curve-fitting and regression analyses to a data set without these extremely humid regions.

We tested for effects of WI, PET, vegetation type, Ks, seasonality of rainfall, and accompanying interactions on recharge using multiple regression analyses. Because of heteroscedasticity, we logarithmically transformed recharge and examined appropriate models to relate recharge to each of the predictor variables. The Breusch–Pagan test was used to test for homoscedasticity, and logarithmic transformation of recharge gave the most homoscedastic relationships with the predictor variables out of all the transformations of recharge values (untransformed, natural logarithm, and square root). We examined appropriate models (linear, exponential, and logarithmic) to relate recharge to the predictor variables individually and found that a logarithmic model explained the most statistical variation in the logarithmically transformed recharge using WI and PET and that a linear model maximized the fit of the logarithmically transformed recharge with Ks and the seasonality of rainfall (amplitude and phase). Thus, we logarithmically transformed WI and PET to linearize them with respect to the logarithmically transformed recharge for the multiple regression. We used WI and PET instead of PWE or AI for our multiple regressions to tease out the relative importance of WI and PET. Stepwise regression with whole effects was used to determine which main and interaction terms to retain in the model and to determine the relative importance of each term for recharge.

Finally, to test the reliability and predictive capability of our regression model, we used threefold cross-validation, in which a model based on a subset of the data is tested against the remainder of the data (Kohavi, 1995).

Site Description

In addition to the literature synthesis, we collected an extensive new field data set as an independent test of our global data set, using paired comparisons of adjacent vegetation types in Argentina and the United States. In Argentina, we located six sites in the Pampas on relatively level landscapes across a precipitation gradient that ranged from 382 to 1215 mm yr−1. Where available, rainfed cropland and woody plant invasion (WPI) plots were paired with an adjacent or nearby (<1 km) natural grassland plot at each site. Cultivation and WPI plots correspond to cropland and woodland vegetation designations, respectively, in our literature synthesis (Tables 1 and 2).

Table 1.

Site information for our new field data in Argentina and the southwestern United States.

SiteLatitude, longitudePrecipitationSoilVegetation typeTime since change§
degreesmm/yryr
Nahuel Mapa−34.8, −66.2382fine sandG, W80
Caldenadas−33.8, −65.8506fine sandG, W, C60, 10
Dixonville−34.7, −65.5525fine sandG, W, C60, 15
Parera−35.1, −64.5682loamG, W, C100+, 80
San Claudio−35.9, −61.21011sandy loamG, C40
San Antonio−34.2, −59.41219loamG, W, C40, 60
Sevilleta34.3, −106.7277Turney loam, sandy loamsG, W50
Goodwell36.6, −101.6407Gruver clay loamG, C, C+I60, 60
Tribune38.5, −101.6479Richfield silt loamG, C, C+I30, 50
San Angelo31.4, −101.3514Angelo clay loamG, C, C+I100, 40
Quanah34.3, −99.8679Sagerton clay loamG, C, C+I100, 60
Vernon33.9, −99.4660Tillman clay loamG, W40
Riesel31.5, −96.9840Heiden clayG, W, C100+, 100+
Engeling31.9, −95.91070loamy fine sandG, W50
SiteLatitude, longitudePrecipitationSoilVegetation typeTime since change§
degreesmm/yryr
Nahuel Mapa−34.8, −66.2382fine sandG, W80
Caldenadas−33.8, −65.8506fine sandG, W, C60, 10
Dixonville−34.7, −65.5525fine sandG, W, C60, 15
Parera−35.1, −64.5682loamG, W, C100+, 80
San Claudio−35.9, −61.21011sandy loamG, C40
San Antonio−34.2, −59.41219loamG, W, C40, 60
Sevilleta34.3, −106.7277Turney loam, sandy loamsG, W50
Goodwell36.6, −101.6407Gruver clay loamG, C, C+I60, 60
Tribune38.5, −101.6479Richfield silt loamG, C, C+I30, 50
San Angelo31.4, −101.3514Angelo clay loamG, C, C+I100, 40
Quanah34.3, −99.8679Sagerton clay loamG, C, C+I100, 60
Vernon33.9, −99.4660Tillman clay loamG, W40
Riesel31.5, −96.9840Heiden clayG, W, C100+, 100+
Engeling31.9, −95.91070loamy fine sandG, W50

Soil texture based on samples from the top 1 m of the soil profile.

G, grassland; W, woodland; C, cropland; C+I, irrigated cropland.

§

Number of years since conversion of grassland to another land use. The numbers listed correspond to the order of land-use changes given in the previous column.

Table 2.

Comparison of the proportion of water input that becomes recharge (R/WI) and potential water excess (PWE) among vegetation and soil types (mean ± standard error).

ParameterR/WIPWEn
mm/yr
Vegetation type
 Crop0.111 ± 0.007 a−677 ± 32 a220
 Grass0.083 ± 0.009 b−637 ± 41 a138
 Scrub0.049 ± 0.011 c−1116 ± 56 b73
 Wood0.062 ± 0.009 c−475 ± 46 c109
 No vegetation0.178 ± 0.03 a−1009 ± 77 b39
Soil type
 Clays0.073 ± 0.007 a−606 ± 36 a205
 Sands0.103 ± 0.006 b−763 ± 26 b374
ParameterR/WIPWEn
mm/yr
Vegetation type
 Crop0.111 ± 0.007 a−677 ± 32 a220
 Grass0.083 ± 0.009 b−637 ± 41 a138
 Scrub0.049 ± 0.011 c−1116 ± 56 b73
 Wood0.062 ± 0.009 c−475 ± 46 c109
 No vegetation0.178 ± 0.03 a−1009 ± 77 b39
Soil type
 Clays0.073 ± 0.007 a−606 ± 36 a205
 Sands0.103 ± 0.006 b−763 ± 26 b374

Different letters indicate a significant difference between each pair within the vegetation or soil type using a Kruskal–Wallis test for R/WI (P < 0.0061 for all significantly different comparisons except between cropland and no vegetation, see below) and Student's t-test for PWE (P < 0.023).

Comparison of proportional recharge between cropland and no vegetation is marginally significant (P < 0.07).

We also selected five sites along a precipitation gradient (407–860 mm yr−1) in the southern Great Plains of the United States. Land uses selected as paired plots were natural grasslands, rainfed croplands, and irrigated croplands. In both the U.S. and Argentina, most plots had >30 yr of relatively continuous land-use history (Table 1). Landowners or farm managers were surveyed for land-use history at each site, including cropping schemes (species and rotations) and fertilizer, pesticide, and irrigation inputs. Tree stand ages were verified with aerial photos or tree ring cores taken during our sampling campaign (2008–2010). Precipitation data were obtained from long-term (>30-yr) records maintained by weather stations onsite by the farm managers or from separate stations 1 to 30 km away (Instituto Nacional de Tecnología Agropecuaria, www.inta.gov.ar/index.asp [verified 23 Oct. 2011]; National Climatic Data Center, www.ncdc.noaa.gov/oa/ncdc.html [verified 23 Oct. 2011]).

In addition to our new field data, we also estimated additional recharge rates based on soil Cl data from five paired grassland and woody-encroached sites located across a precipitation gradient in the southwestern United States. Detailed descriptions for these sites are available in Jackson et al. (2002) and McCulley et al. (2004). We collectively refer to these and the southern Great Plains sites as our southwestern U.S. sites.

Soil Sampling

At the Argentinean sites, soil samples were taken by augering three to eight boreholes 6 to 9 m deep or to the depth of groundwater, as well as four to six shallow cores (30 cm deep), at each land-use plot. Augered samples were taken every 20 cm to the 1-m depth, then every 30 cm to the 4-m depth, then every 50 cm. The soil samples were homogenized and subsampled in the field and then frozen until analysis.

At our U.S. sites, we used a direct-push mechanical coring rig (Geoprobe Systems, Salina, KS) for five to eight cores per plot to a 8.5-m depth. At only one plot near San Angelo, TX, were soil samples not retrieved to 8.5 m because of indurated caliche found around the 5-m depth that blocked further coring. The soil cores were weighed in the field, subsampled for soil moisture and bulk density using intact cores and for elemental analysis using homogenized soil cores, then shipped to Duke University for analysis.

In the laboratory, the soil samples were oven dried for gravimetric moisture content and analyzed for chemical constituents. Dried and homogenized soil samples were mixed with double deionized water at a 1:1 (w/w) ratio and shaken for 4 h. The mixture was centrifuged, the supernatant filtered, and the filtrate analyzed for anion contents (Cl, Br, NO3, SO42−, and PO43−) by ion chromatography (Dionex ICS-2000, Sunnyvale, CA). The Cl concentrations in the soil pore water were calculated by dividing the soil Cl contents (mg Cl kg−1 soil) by the gravimetric soil moisture. Soil texture was determined by the pipette method (Klute, 1986) and ranged from sand to clay (Table 1).

Groundwater Recharge Calculations

Recharge rates at our sites were estimated by Cl mass balance from soil samples in the unsaturated zone (Allison and Hughes, 1983). Total atmospheric inputs of Cl were obtained from Piñeiro et al. (2007) and Santoni et al. (2010) for the Argentinean sites and from deposition networks in the United States (National Atmospheric Deposition Program, National Trends Network, nadp.sws.uiuc.edu/ntn/ [verified 23 Oct. 2011]; Clean Air Status and Trends Network, java.epa.gov/castnet/ [verified 23 Oct. 2011]). To estimate Cl deposition rates at our sites, we used the distance from the ocean (Junge and Werby, 1958; Keywood et al., 1997), which correlated well with Cl deposition for our Argentine and U.S. study regions (Supplementary Fig. 1; P < 0.001, 0.001; n = 12, 6; R2 = 0.99, 0.99 for U.S. and Argentine sites, respectively). Dry deposition at U.S. sites was estimated based on the relationship between the dryfall/wetfall ratio in precipitation across the study region (Supplementary Fig. 2; P < 0.001, n = 9, R2 = 0.82). Anthropogenic inputs of Cl due to cultivation were calculated by multiplying the Cl contents of the fertilizer, pesticide, and irrigation samples obtained at the sites with the average application rates revealed in the surveys (Table 2). Assuming steady-state conditions, the recharge rate was calculated using the following mass balance equation:
graphic
where Qin is the average volume of rain and irrigation water entering the root zone per ground area per year (mm3 mm−2 yr−1), Clin is the average atmospheric and anthropogenic Cl input expressed as the concentration in precipitation (mg mm−3), Qout is the volume of water exiting the root zone per ground area per year (mm3 mm−2 yr−1), and Clout is the concentration of Cl in the soil water exiting the root zone (mg mm−3). Assuming no dispersion and diffusion of Cl and assuming Clout to be the average Cl concentration of the soil pore water below the root zone, Qout is the groundwater recharge rate (mm yr−1). The approximate root zone was taken to be the top 2.1 m, below which we found a linear relationship between cumulative Cl and cumulative soil moisture content, except for some cultivated plots where we assumed the root zone to be the top 1 m (Phillips 1994). At the Tribune, Vernon, and Riesel sites, where we did not observe complete leaching of the original Cl peak with cultivation, we also used the Cl displacement method to calculate recharge rates based on the migration of the original grassland Cl and changes in water profiles (Walker et al., 1991). Calculations for the Cl tracer displacement (CTD) method were
graphic
where QCTD is the recharge rate (mm yr−1), z1 and z2 are the depths (mm) of the Cl fronts corresponding to land uses at years t1 (new, rainfed cultivation) and t2 (old, grassland), and θ is the average soil moisture content of this depth interval. A value of 8.5 m was used as z1 for profiles without a clear Cl peak, providing a lower bound estimate for recharge.
We compared results from our global data set and independent field data to estimate the influence of vegetation shifts on recharge for different climatic and soil conditions globally. We calculated the absolute and relative changes in recharge with land-use changes. For the field data, the relative change (Δ) was defined as
graphic
where rechargeveg1 and rechargeveg2 are recharge estimates under two different vegetation types. Grassland was the original vegetation at our field sites, and grassland recharge values were used for rechargeveg2.

For the global synthesis data, we used recharge predicted from the best-fit curves to calculate the absolute and relative differences in recharge among vegetation types.

Vegetation and soil types had strong effects on the proportional recharge (R/WI) globally (χ2 = 73.7 and 13.9, respectively; P < 0.0002). On average, proportional recharge was 0.18, 0.11, 0.08, 0.06, and 0.05 under NoVeg, croplands, grasslands, woodlands, and scrublands, respectively (P < 0.0005 for all pairwise comparisons except scrublands to woodlands and grasslands to croplands; Table 2). Sandy soils had 50% more proportional recharge as clayey soils, on average.

Potential water excess fitted to an exponential model was the best single predictor of recharge across the data set (Fig. 2). Recharge increased with PWE for croplands, grasslands, and woodlands in both sands and clays (average R2 = 0.52, P < 0.0001 for all vegetation–soil types; Fig. 2). Differences among vegetation types were evident in the fitted curves. For example, at a PWE of −250 mm yr−1 in clays, the predicted recharge under croplands, grasslands, and woodlands were 112, 61, and 35 mm yr−1, respectively (n = 220, 138, and 109, respectively).

Fig. 2.

Recharge and potential water excess fitted to an exponential model for three vegetation types and two soil types.

Fig. 2.

Recharge and potential water excess fitted to an exponential model for three vegetation types and two soil types.

Water inputs in the multiple regression explained 29% of the statistical variation in recharge across the data set (P < 0.0001; Table 3). Other significant variables in the order of decreasing importance were vegetation type (16%, P < 0.0001), PET (12%, P < 0.0001), and Ks (6%, P < 0.0001), with amplitude and phase (seasonality) of rainfall contributing a statistically significant but minor 1% of the variation (P < 0.0001; Table 3). Recharge increased with increasing WI, Ks, and seasonality of rainfall and decreased with increasing PET. Overall, recharge was greatest under croplands, about two and 15 times greater than under grasslands and woodlands, respectively (P < 0.0001; Table 3, Veg term).

Table 3.

Results from stepwise and least squares multiple regressions of logarithmically transformed recharge. Factors in the stepwise regression were selected using the Bayesian information criterion (Schwarz, 1978).

Stepwise
F test
TermsParameter estimatesSeq. SS§R2F ratioP
Intercept2.87 ± 1.88
log(WI)2.71 ± 0.1596260.29292<0.0001
Veg1.19 ± 0.0762, 0.419 ± 0.0857, −1.61 ± 0.08963360.45193<0.0001
log(PET)−2.61 ± 0.1902600.57189<0.0001
Ks0.0002336 ± 0.00002581340.6382<0.0001
Veg × log(WI)−1.01 ± 0.157, −0.510 ± 0.178, 1.52 ± 0.188)1190.6937<0.0001
Veg × log(PET)0.993 ± 0.219, 0.172 ± 0.242, −1.17 ± 0.237430.7116<0.0001
Ks × phase−0.0000597 ± 0.0000131170.7221<0.0001
log(PET) × Ks0.000304 ± 0.000071220.7318<0.0001
Amplitude0.00408 ± 0.00105120.73150.0001
Veg × Ks10−5(−11.6 ± 3.38, 8.17 ± 3.58, −3.39 ± 3.79)180.7460.0020
log(WI) × phase−0.188 ± 0.0668100.7580.0051
Phase0.0577 ± 0.028260.7540.0413
Stepwise
F test
TermsParameter estimatesSeq. SS§R2F ratioP
Intercept2.87 ± 1.88
log(WI)2.71 ± 0.1596260.29292<0.0001
Veg1.19 ± 0.0762, 0.419 ± 0.0857, −1.61 ± 0.08963360.45193<0.0001
log(PET)−2.61 ± 0.1902600.57189<0.0001
Ks0.0002336 ± 0.00002581340.6382<0.0001
Veg × log(WI)−1.01 ± 0.157, −0.510 ± 0.178, 1.52 ± 0.188)1190.6937<0.0001
Veg × log(PET)0.993 ± 0.219, 0.172 ± 0.242, −1.17 ± 0.237430.7116<0.0001
Ks × phase−0.0000597 ± 0.0000131170.7221<0.0001
log(PET) × Ks0.000304 ± 0.000071220.7318<0.0001
Amplitude0.00408 ± 0.00105120.73150.0001
Veg × Ks10−5(−11.6 ± 3.38, 8.17 ± 3.58, −3.39 ± 3.79)180.7460.0020
log(WI) × phase−0.188 ± 0.0668100.7580.0051
Phase0.0577 ± 0.028260.7540.0413

WI, water inputs; Veg, vegetation type; PET, potential evapotranspiration; Ks, saturated hydraulic conductivity; phase, number of months between maximum mean monthly precipitation and temperature; amplitude, difference between maximum and minimum mean monthly precipitation. Interaction terms involving continuous variables are centered around their means for computational purposes, e.g., Veg × [log(WI) − 6.39]. Average values for the interaction terms were 6.39, 7.11, 1912, and 3.19 for log(WI), log(PET), Ks, and phase, respectively.

Parameter estimates are given for the three vegetation types (cropland, grassland, and woodland, respectively) ± standard errors.

§

Sequential sum of squares.

The interaction terms of vegetation type with climate or soil variables collectively explained an additional 8% of the variation in recharge (Table 3; Fig. 3). Of all the vegetation types, cropland recharge increased the most with WI, but grassland recharge increased the most with increasing Ks and decreasing PET. In contrast, woodland recharge was the least sensitive to Ks and PET, indicating that recharge under different vegetation types responded differently to climate and soil factors. These responses accentuated the differences in recharge among vegetation types in humid regions and in sandy soils (Fig. 3a, 3b, and 3c). The cross-validation analysis of the regression model and the data set produced comparable results, giving confidence in the model's reliability (Supplementary Fig. 3).

Fig. 3.

(a, b, c) Predicted recharge from interaction of vegetation type and physical variables of (a) water input (WI), (b) potential evapotranspiration (PET), and (c) saturated hydraulic conductivity (Ks) in the multiple regression analysis, and (d, e, f) logarithmically transformed recharge fitted to the (d) WI, (e) PET, and (f) Ks in the data set. Recharge values were predicted from the multiple regression model holding all other terms constant around their means. Logarithmically transformed recharge was fitted without data at the very highest values of WI due to insufficient data across vegetation types. Note the different y axis scales.

Fig. 3.

(a, b, c) Predicted recharge from interaction of vegetation type and physical variables of (a) water input (WI), (b) potential evapotranspiration (PET), and (c) saturated hydraulic conductivity (Ks) in the multiple regression analysis, and (d, e, f) logarithmically transformed recharge fitted to the (d) WI, (e) PET, and (f) Ks in the data set. Recharge values were predicted from the multiple regression model holding all other terms constant around their means. Logarithmically transformed recharge was fitted without data at the very highest values of WI due to insufficient data across vegetation types. Note the different y axis scales.

Our new field data set from central Argentina and the southwestern United States independently confirmed the strong differences in recharge for croplands, grasslands, and woodlands that the global synthesis revealed. Croplands had significantly lower average soil pore water Cl concentrations below the root zone, while woodland plots had significantly higher soil pore water Cl compared with their grassland pairs (Table 4; Supplementary Fig. 4; signed Wilcoxon test; P < 0.0020 and 0.0039 for grassland–cropland and grassland–woodland comparisons, respectively). This result indicated that the greatest recharge occurred under croplands, intermediate recharge occurred under grasslands, and the lowest recharge occurred under woodlands. This strong biological control over soil water fluxes is in close agreement with our global review (Fig. 4; Table 4). Crop cultivation using groundwater as an irrigation source resulted in a very high net discharge of groundwater (Table 4).

Fig. 4.

Absolute and relative differences and changes in recharge between grassland, cropland, and woody vegetation from synthesis and field data. Solid lines and filled symbols denote absolute differences in recharge; dashed lines and open symbols denote relative differences. Fitted lines for the field data (bottom panels) were chosen from linear regressions on logarithmically transformed or untransformed differences in recharge.

Fig. 4.

Absolute and relative differences and changes in recharge between grassland, cropland, and woody vegetation from synthesis and field data. Solid lines and filled symbols denote absolute differences in recharge; dashed lines and open symbols denote relative differences. Fitted lines for the field data (bottom panels) were chosen from linear regressions on logarithmically transformed or untransformed differences in recharge.

Table 4.

Chloride inputs and soil Cl values used for the groundwater recharge calculations.

SiteVegetation typeNatural Cl inputsIrrigationAnthropogenic Cl inputsSoil water Cl§Recharge rate
mg/Lmm/yrmg/Lmm/yr
Nahuel Mapagrassland0.43534.54.81
woodland142.61.16
Caldenadasgrassland0.28419.57.38
woodland62.62.30
rainfed cropland11.712.3
Dixonvillegrassland0.3579.220.5
woodland17.111.4
rainfed cropland6.528.8
Pareragrassland0.3387.7529.7
woodland9.723.8
rainfed cropland4.057.6
San Claudiograssland0.29013.521.0
rainfed cropland7.541.0
San Antoniograssland0.30812.130.1
woodland16.322.4
rainfed cropland4.679.0
Sevilletagrassland0.24418750.037
woodland44290.012
Goodwellgrassland0.1095640.078
rainfed cropland0.00138.81.45
irrigated cropland43216.524759.6
Tribunegrassland0.0892550.317
rainfed cropland30.31.55 (4.76)
irrigated cropland5845.347.4146
San Angelograssland0.1942660.47
rainfed cropland45.63.17
irrigated cropland25411270960.0
Quanahgrassland0.1492780.41
rainfed cropland703.24 (6.36)
irrigated cropland6102431071279
Vernongrassland0.16119250.06
woodland39000.03
Rieselgrassland0.2971372.4
rainfed cropland377.2 (9.0)
woodland3300.76
Engelinggrassland0.30211.728.8
woodland23.114.0
SiteVegetation typeNatural Cl inputsIrrigationAnthropogenic Cl inputsSoil water Cl§Recharge rate
mg/Lmm/yrmg/Lmm/yr
Nahuel Mapagrassland0.43534.54.81
woodland142.61.16
Caldenadasgrassland0.28419.57.38
woodland62.62.30
rainfed cropland11.712.3
Dixonvillegrassland0.3579.220.5
woodland17.111.4
rainfed cropland6.528.8
Pareragrassland0.3387.7529.7
woodland9.723.8
rainfed cropland4.057.6
San Claudiograssland0.29013.521.0
rainfed cropland7.541.0
San Antoniograssland0.30812.130.1
woodland16.322.4
rainfed cropland4.679.0
Sevilletagrassland0.24418750.037
woodland44290.012
Goodwellgrassland0.1095640.078
rainfed cropland0.00138.81.45
irrigated cropland43216.524759.6
Tribunegrassland0.0892550.317
rainfed cropland30.31.55 (4.76)
irrigated cropland5845.347.4146
San Angelograssland0.1942660.47
rainfed cropland45.63.17
irrigated cropland25411270960.0
Quanahgrassland0.1492780.41
rainfed cropland703.24 (6.36)
irrigated cropland6102431071279
Vernongrassland0.16119250.06
woodland39000.03
Rieselgrassland0.2971372.4
rainfed cropland377.2 (9.0)
woodland3300.76
Engelinggrassland0.30211.728.8
woodland23.114.0

Expressed as milligrams per liter of precipitation.

Cl inputs from fertilizers, pesticides, and irrigation, expressed as milligrams per liter total water input (precipitation + irrigation). Most of the 30+ agricultural chemicals analyzed were not significant sources of Cl. – denotes negligible Cl inputs (<0.001 mg/L) from fertilizer and pesticide applications.

§

Average Cl concentration in the soil pore water below the root zone (>2.1 m)

Average recharge rates based on Cl mass balance, with those based on the Cl tracer displacement method in parentheses.

Our field data set also confirmed the interactive effect of vegetation and climate on recharge that the global synthesis revealed. For our global synthesis, absolute differences in recharge among vegetation types using PWE as the best-fit predictor variable were small in arid climates and grew with increasing PWE and were larger in sandy soils than in clays between grassland and woodland (Fig. 3 and 4). Relative differences were largest in arid climates and in clays (Supplementary Fig. 5), however, suggesting that proportionately greater hydrologic effects of land use change may occur in more arid regions and in clayey soils. Similarly to the global synthesis, our new field estimates of recharge gains or losses due to land-use conversions of natural grasslands increased in magnitude with PWE (Fig. 4), revealing interactions between land use and the abiotic environment in determining recharge. As in the global synthesis, our field-based estimates of relative changes in recharge showed an increasing importance of vegetation effects toward lower precipitation and higher clay content areas, suggesting that while land-use changes have the potential to change recharge by large amounts in humid regions and coarse-textured soils, vadose zone processes may be particularly sensitive to land-use changes in relatively arid areas and fine-textured soils (Fig. 4; Supplementary Fig. 5).

Although the role of vegetation in global terrestrial water fluxes is well recognized (Hutjes et al., 1998; Kucharik et al., 2000; Arora, 2002; Jackson et al., 2005), this synthesis is, to our knowledge, the first attempt to quantify the relative importance of vegetation on recharge rates globally. Vegetation was the second most powerful predictor of recharge after WI, explaining about 1.3 and three times as much variation in recharge as PET and Ks, respectively, indicating that vegetation type is often more important for determining recharge than most physical variables (Table 3). As a result, vegetation should be one of the key components of analyses or models addressing scales sufficiently large to include multiple vegetation types.

The treatment of vegetation parameters in global land-surface models are sometimes cursory and are rarely process based with regard to recharge (Gerten et al., 2004). Our global synthesis should help parameterize such models and could contribute as inputs or could be used for independent testing of global water balance or climate models. For example, studies modeling the reciprocal effects of groundwater on climate (e.g., Niu et al., 2007) may benefit from better constrained estimates of recharge under different vegetation types.

Changes in recharge with land-use changes in our field data followed the patterns of recharge observed under different vegetation and soil types across our global synthesis (Fig. 4; Supplementary Fig. 5). Overall, agreement between the field and synthesis results suggests that vegetation is responsible for a large portion of the variation in recharge and that distinct patterns of recharge among vegetation types are typically clear and reproducible when covarying site factors such as soil properties are controlled for. Agricultural conversion of grasslands or woodlands would therefore probably bring about greater recharge, whereas woody plant invasion or afforestation into grasslands or croplands would probably reduce recharge. These hydrologic changes may be especially severe for land-use changes to and from woodlands because the capacity of woody plants to limit recharge leads to large differences in recharge between woodland and the other vegetation types (Fig. 2; Table 3). Loss of renewable water yield to planted or invading woody plants could be detrimental to groundwater-dependent communities, both human and natural, across long time scales. In contrast, cultivation generally increases recharge but may pose a risk of salinization or degradation of groundwater quality in some regions through associated leaching of salts through the vadose zone (Smettem, 1998; Boumans et al., 2005; Jobbágy and Jackson, 2007; Scanlon et al., 2007a). Such disruptions to the hydrologic cycle should be recognized in land management and policy decisions.

The effect of vegetation on recharge was further evident along the entire climate gradient and across soil types (Fig. 2). In our synthesis, we observed large absolute differences in recharge among vegetation types in mesic regions (high WI, low PET) and in sands (high Ks) but larger relative differences in arid climates and in clays (Fig. 4; Supplementary Fig. 5). Relative differences between grasslands and the other vegetation types in clay soils, for example, were as much as −70 and 250% (woodlands and croplands, respectively) in arid climates compared with only −20 and 60% in humid areas (Supplementary Fig. 5). Although the large absolute differences in recharge among vegetation types in humid climates highlight the importance of land-use changes on water yields in these climates, large relative differences in drier climates forecast proportionately important hydrologic changes in arid regions, as observed previously for stream flow (Farley et al., 2005). Mirroring the synthesis data, the observed 70% reduction in grassland recharge with woody plant invasion and >500% gain in recharge with cultivation of arid grasslands with clayey soils in our field data indicate that near-complete loss of groundwater recharge or flushing of accumulated vadose zone solutes may be possible with land-use changes (Fig. 4; Supplementary Fig. 5). The different responses of recharge among vegetation types to climate and soils warrant careful consideration of these interactions to avoid adverse hydrologic consequences of land-use changes.

Vegetation type explained a similar amount of variation in recharge as important physical variables did, and its interactions with physical variables contributed additional explanatory power. Recharge was correlated with high Ks (Table 3), but we observed this effect primarily in grasslands, which have relatively shallow root systems (2.5 m; Canadell et al., 1996). The analogous increases in woodland recharge were less pronounced. Woody plants grow deeper roots in areas with sandy soils (high Ks), in part to capture water throughout the soil profile (Schenk and Jackson, 2002, 2005); these deep woody roots may limit recharge despite higher Ks. In croplands, with the shallowest expected rooting depth (generally <2 m), recharge was generally higher than for other vegetation types but did not vary substantially with Ks. This result may be due to particular management practices in croplands, such as tillage increasing deep drainage and weakening the overall positive effect of Ks on recharge under croplands (Daniel 1999; Scanlon et al., 2008). Interactions between vegetation and physical variables such as Ks and PET collectively explained >8% of the statistical variation in recharge and helped identify potential mechanisms responsible for differences in recharge among vegetation types.

Irrigation is often used to enhance crop productivity and to meet increasing food demands given decreasing available productive land area (Kendall and Pimentel, 1994), but it also causes a large net discharge of groundwater, as we observed at our southwestern U.S. sites. Assuming that rainfed croplands represent the upper limits for recharge and irrigation uses groundwater, we consider the difficult issues of irrigation and sustainable groundwater use from a land management perspective. We observed from our global synthesis that, despite being the land use with the highest recharge, rainfed cultivation allowed only marginal recharge compared with the net discharge (irrigation − recharge) of irrigated cultivation (Fig. 5). Across a gradient of water availability, the area of rainfed cultivation needed to sustainably supply groundwater for 1 ha of irrigated agriculture decreases from 70 ha in arid climates to 0.5 ha in humid climates (Fig. 5), providing first-order approximation of the irrigated/rainfed cropland ratio necessary for sustainable groundwater management. It points to challenges associated with providing enough groundwater for irrigated crops, especially in more arid regions where the lack of rainwater results in both larger irrigation needs and lower recharge rates under rainfed cultivation.

Fig. 5.

Recharge under rainfed cropland (Rr) and net discharge (D = irrigation minus recharge) under irrigated cropland across water availability (precipitation − potential evapotranspiration [PET]). Net discharge under irrigated crops is up to two orders of magnitude greater than recharge. The ratio of D/Rr represents the land area of rainfed cultivation needed to provide a unit area of irrigated cultivation.

Fig. 5.

Recharge under rainfed cropland (Rr) and net discharge (D = irrigation minus recharge) under irrigated cropland across water availability (precipitation − potential evapotranspiration [PET]). Net discharge under irrigated crops is up to two orders of magnitude greater than recharge. The ratio of D/Rr represents the land area of rainfed cultivation needed to provide a unit area of irrigated cultivation.

For the range of PWE and the recharge values analyzed, the exponential model gave the best overall fit but should not be extrapolated beyond the ranges presented in this study. For instance, with inclusion of the very limited data from perhumid regions, the sigmoidal model gave the best overall fit (data not shown), indicating that the increase in recharge with PWE may taper off in very humid regions due to the increasing importance of runoff on the water balance (Milly, 1997). Moreover, irrigation reported at most of the sites were often estimates without long-term monitoring, introducing uncertainty in our observed relationship between recharge and WI. The average uncertainty associated with irrigation from studies that reported ranges of irrigation was about 190 mm yr−1. Although the effect of this uncertainty on the estimated parameters of our multiple regression were not statistically significant (data not shown), the large explanatory power of WI in our model highlights the importance of obtaining the best possible irrigation and precipitation data for recharge predictions.

In conclusion, vegetation and its interactions with other factors have a strong effect on groundwater recharge, explaining ∼24% of the global variation in recharge—more than other variables except WI. An average of 11% of WI becomes recharge under croplands, whereas only 8 and 6% do under grasslands and woodlands, respectively. Vegetation types had predictable effects on groundwater recharge, and the differences in recharge among vegetation types also varied predictably across the climate and soil variables. Independent field estimates of recharge under paired land-use plots confirmed our global synthesis results and provided a direct test of the relationships between vegetation and recharge. Significant gains and losses in recharge are possible with conversion to crops and to forests, respectively, and absolute changes in water yield accompanying land-use changes are likely to be larger in humid or sandy areas. Proportionately large relative hydrologic consequences result from land-use changes in arid or clayey regions, however, as observed previously for stream flow (Farley et al., 2005). Quantifying and predicting changes to water yield from land-use changes are necessary steps for sustainable and holistic management of water resources; our results highlight the importance of land-use change for the vadose zone and groundwater resources.

Table A1.

Recharge estimates, site information, and values used for multiple regression analyses.

ReferenceLat., long.Soil texture or KsVegetationRechargePrecipitationIrrigationPET§Amplitude§Phase§Methods
mm yr−1mm mo−1mo
Abdalla (2008) 11.1, 32.6sandscrubland0.94001930154.24model
11.1, 29.1clayscrubland410251893147.44model
16.1, 34.6sandscrubland7.3130210272.92model
Ahmed and Umar (2008) 29.4, 77.3claycropland2056685501454267.22WTF
29.4, 77.3claycropland2806688001454267.22WTF
29.4, 77.3claycropland3006688001454267.22WTF
Allen (1981) −31.8, 115.9sandscrubland857751661155.54WB
Allison and Hughes (1972) −37.8, 140.8sandgrassland63686113286.35T
−37.8, 140.8sandwoodland13686113286.35T
Allison and Hughes (1978) −37.8, 140.8sandgrassland106700113286.35T
−37.8, 140.8sandgrassland114700113286.35T
Allison and Hughes (1983) −35.1, 142.1sandcropland3.5335137915.55T
−35.1, 142.1sandwoodland0.07335137915.55T
Allison et al. (1985) −34.3, 139.6sandwoodland0.135300137414.83T
Allison et al. (1990) −36.3, 140.8claycropland25001245446T
−36.3, 140.8claycropland25001245446T
−34.3, 139.6sandcropland13300137414.83T
−35.1, 140.3sandy loamcropland25.23701346225T
−34.3, 139.6sandwoodland0.05300137414.83T
−35.1, 140.1sandwoodland0.05340133523.15T
−35.1, 141.9sandwoodland0.06340137315.75T
−35.1, 140.3sandwoodland0.073701346225T
−34.4, 140.1sandy loamwoodland0.07270136115.26WB
−35.1, 140.3sandwoodland0.643701346225WB
−34.4, 140.1sandwoodland1.3270136115.26T
Al-Sagaby andMoallim (2001)25.8, 42.9sandno vegetation1.81332283414T
Amro et al. (2001) 29.8, 35.3sandno vegetation0.03651768105T
32.1, 36.1sandy siltno vegetation0.267150451.65T
32.1, 36.1sandy siltno vegetation1.567150451.65T
32.3, 35.9sandno vegetation8480144777.35T
32.3, 35.9sandno vegetation28480144777.35T
Anderson et al. (1998) −30.6, 116.1loamcropland214703174075.24WB
Andrews et al. (1997) 52.3, 0.4sandcropland8347448120.14WTF
52.3, 0.3clay loamcropland10445548120.41WTF
Anuraga et al. (2006) 13.1, 78.3claycropland8490215301604model
12.9, 78.3claycropland909024101529160.94model
13.1, 78.3claycropland12490215015301604model
13.1, 78.3sandy loamcropland18490215301604model
13.1, 78.3sandy loamcropland22090241015301604model
13.1, 78.3sandy loamcropland23290215015301604model
Athavale et al. (1980) 16.9, 78.6claycropland6711001669177.34T
16.9, 78.6claycropland7311501669177.34T
16.9, 78.6claycropland809701669177.34T
16.9, 78.6sandy clay loamcropland8313101669177.34T
16.9, 78.6sandy loamcropland8311501669177.34T
16.9, 78.6claycropland96.813101669177.34T
16.9, 78.6sandy loamcropland9812001669177.34T
16.9, 78.6sandy loamcropland13313101669177.34T
16.9, 78.6sandy loamcropland22214301669177.34T
Babiker et al. (2005) 35.4, 136.9sandcropland8601915895195.12WB
Beekman et al. (1996) −22.1, 26.3sandscrubland12.5500140883.80T
Bekele et al. (2006) −29.8, 115.6sandcropland14.74401869109.54T
−29.8, 115.6sandcropland35.74401869109.54WTF
−29.8, 115.6sandgrassland16.24401612109.54T
−29.8, 115.6sandgrassland35.94401612109.54T
−29.8, 115.6sandscrubland94401869109.54T
Bellot et al. (1999) 38.3, −0.6loamgrassland61.54541136552model
38.3, −0.6loamno vegetation1254541136552model
38.3, −0.6loamscrubland18.64541136552model
38.3, −0.6loamwoodland9.64541136552model
Bent (2001) 42.4, −72.3fine sandy loamwoodland262124885522.54model
42.4, −72.3fine sandy loamwoodland371116985522.54model
Beverly et al. (2005) −37.3, 144.9sandgrassland113651112140.36model
Bird et al. (2004) −37.8, 142.1clay loamcropland366951149576WB
−37.8, 142.1clay loamgrassland186951149576WB
Bredenkamp and Vandoolaeghe (1982) −33.6, 18.4coarse sandsscrubland73.5350123673.25model
−33.6, 18.4coarse sandsscrubland95350123673.25WB
Butler and Verhagen (2001) −27.1, 22.8sandgrassland1.8337157265.62T
−27.1, 22.8sandgrassland13337157265.62T
Calder et al. (2003) 53.3, −1.1sandgrassland16980044914.65model
53.3, −1.1sandscrubland15680044914.65model
53.3, −1.1sandwoodland3064344914.65T
53.3, −1.1sandwoodland45.864344914.65model
53.3, −1.1sandwoodland6964344914.65WB
53.3, −1.1sandwoodland10664344914.65model
53.3, −1.1sandwoodland12064344914.65WB
Carbon et al. (1982) −31.8, 115.9coarse sandsgrassland1738001661155.54WB
−31.8, 115.9coarse sandswoodland1219001661155.54WB, WTF, T
Carlson et al. (1988) 33.3, −99.3clay loamgrassland7671161070.92lysimeter
33.3, −99.3clay loamno vegetation9.3671161070.92lysimeter
33.3, −99.3clay loamwoodland3.3671161070.92lysimeter
Cherkauer and Ansari (2005) 43.3, −88.3sandcropland123103083971.21base flow
Cho et al. (2009) 37.3, −80.112 mm d−1woodland27104598233.60model
Colville and Holmes (1972) −37.6, 140.8sandgrassland82700115186.95WTF
−37.6, 140.8sandwoodland44700115186.95WTF
Conrad et al. (2005) −32.4, 18.8coarse sandscropland15275144058.74T
−32.4, 18.8coarse sandsscrubland2200144058.74T
Cook (1992) −35.1, 140.1loamy sandcropland9.8340133523.15T
Cook and Kilty (1992) −35.1, 140.1sandcropland9340133523.15EMI
Cook et al. (1989) −34.6, 142.8sandy clay loamcropland7312142115.94T
−34.6, 143.6sandy clay loamcropland8.3322137813.86T
−35.1, 140.1sandgrassland2.7340133523.15EMI
−35.1, 140.1sandgrassland17.4340133523.15T
−35.1, 140.1sandwoodland0.05340133523.15T
Cook et al. (1992a) −34.4, 140.1sandy loamcropland3270136115.26T
Cook et al. (1992b) 15.6, −16.3sandcropland153561853130.12T
Cook et al. (1994) −34.3, 139.6sandgrassland11340137414.83T
−35.1, 140.1sandgrassland13340133523.15T
−35.1, 140.1sandgrassland16340133523.15T
−35.1, 140.1sandwoodland0.1260133523.15T
−35.1, 140.1sandwoodland0.9260133523.15T
Cook et al. (1998) −12.6, 131.1claywoodland200172019313722T
Cook et al. (2004) −34.3, 140.6sandgrassland2.72601373143T
−34.3, 140.6sandgrassland4.92601373143model
−34.3, 140.6sandswoodland0.12601373143T
Crosbie et al. (2007) −34.6, 148.8claygrassland5.2613115325.33WTF
−34.6, 148.8claygrassland48.4613115325.33WTF
Dams et al. (2008) 51.3, 4.8sandcropland29283957726.94model
Daniel (1999) 35.6, −98.1loamcropland93.874314241012WTF
35.6, −98.1loamgrassland63.974314241012WTF
Datta et al. (1980) 23.6, 73.3sandy loamcropland348521724307.72T
23.1, 72.6sandy loamcropland35.66481718276.12T
23.1, 72.6sandy loamcropland58.510141718276.12T
23.4, 72.4sandy loamcropland70.913571754256.52T
23.8, 73.1sandy loamcropland8711451758301.12T
23.4, 72.4sandy loamcropland14416821754256.52T
23.1, 73.1sandy loamcropland18414111731325.62T
De Vries et al. (2000) −24.8, 25.3sandscrubland0.9325137699.60T
−24.8, 25.3sandscrubland1350137699.60T
−24.1, 25.3sandscrubland3420137288.20T
−23.8, 25.1sandscrubland5450139681.10T
Deans et al. (2005) 15.6, −16.3sandcropland153561853130.12T
Di and Cameron (2002) −43.8, 171.8silt loamcropland37065068121.95
Dolling et al. (2007) −29.9, 116.6sandcropland30335173253.15model
−33.9, 117.1sandcropland1154961295775model
Dripps and Bradbury (2007) 43.1, −89.6silt loamcropland25683482475.61WB
43.1, −89.6silt loamcropland29083482475.61WB
46.1, −89.8claygrassland27979068884.91WTF
46.1, −89.8claygrassland28779068884.91WB
46.1, −89.8claywoodland13079068884.91WTF
46.1, −89.8claywoodland17579068884.91WB
46.1, −89.8claywoodland17679068884.91WTF
46.1, −89.8claywoodland26879068884.91WB
Duffková (2002) 49.3, 14.8sandy loamgrassland20.652859952.51lysimeter
Dunin et al. (1999) −35.4, 147.6850 mm d−1cropland15611107938.75WB
−35.4, 147.6850 mm d−1cropland84611107938.75WB
−35.4, 147.6850 mm d−1cropland89611107938.75WB
−35.4, 147.6sandy clay loamcropland185611107938.75WB
−35.4, 147.6sandy clay loamgrassland25611107938.75WB
−35.4, 147.6850 mm d−1grassland2611107938.75WB
Dyck et al. (2003)51.9, −107.3silty loamcropland332171948.70T
Edmunds (2001b) 34.8, 32.9sandgrassland52.54201364104.14T
34.8, 32.9sandgrassland55.54201364104.14T
Edmunds and Gaye (1994) 15.9, −16.3claycropland2.692901858107.31T
15.8, −16.3sandcropland14.92901853118.42T
Edmunds et al. (2002) 13.1, 10.1sandno vegetation35.33142286168.43T
Facchi et al. (2005) 45.1, 9.6coarse sandsgrassland49180051267855.93model
Favreau et al. (2009) 13.6, 2.8sandcropland255572160171.63WTF
13.6, 2.8sandscrubland25572160171.63model
Favreau et al. (2002) 13.4, 2.8sandcropland355672152175.13WTF
13.4, 2.8sandscrubland35672152175.13T
Fayer et al. (1996) 46.6, −119.4loamy sandgrassland1.2159108318.55T
46.6, −119.4sandy loamgrassland5.1159108318.55T
46.6, −119.4sandy loamgrassland25.4159108318.55WB
46.6, −119.4coarse sandsno vegetation55.4159108318.55lysimeter
46.6, −119.4gravelno vegetation86.7184108318.55lysimeter
46.6, −119.4gravelno vegetation300480108318.55lysimeter
46.6, −119.4loamy sandscrubland0.02159108318.55T
46.6, −119.4silt loamscrubland0.05159108318.55T
46.6, −119.4loamy sandscrubland2159108318.55T
46.6, −119.4silt loamscrubland2.75159108318.55T
Fillery and Poulter (2006) −30.8, 116.6loamy sandcropland53495164362.75WB
Finch (1998) 51.6, −1.1sandy clay loamcropland29058747325.35WB
51.6, −1.1sandy clay loamgrassland17658747325.35WB
51.6, −1.1sandy clay loamwoodland9658747325.35WB
Fisher and Healy (2008) 46.3, −119.9silty claycropland119187744106822.55lysimeter, WB
39.3, −76.1fine sandy loamcropland315981104527.61lysimeter, WB
37.3, −120.8sandcropland4232701200138448.46lysimeter, WB
39.8, −85.8silty clay loamcropland47590695568.40lysimeter, WB
Fouty (1989) 36.9, −116.8loamy sandscrubland0.23104175412.25T
Gates et al. (2008) 39.9, 101.9sandgrassland1.584101017.51T
Gaye and Edmunds (1996) 15.8, −16.4sandcropland242901843118.32T
15.8, −16.4sandcropland31.52901843118.32T
Gee et al. (1994) 32.6, −106.4loamy fine sandno vegetation87338170450.81lysimeter, WB
Gee et al. (1993) 46.6, −119.4sandno vegetation71.1172108318.55lysimeter, WB
46.6, −119.4sandno vegetation300480108318.55lysimeter, WB
George and Frantom (1988) −31.6, 118.3sandy claywoodland0.13281501425T
−31.6, 118.3sandy claywoodland1.53281501425T
Gieske (1992) −24.4, 25.6sandscrubland10492135794.10T
Gieske et al. (1995)−24.3, 25.3sandscrubland9425137291.60T
−24.3, 25.3sandscrubland15425137291.60T
Goni and Edmunds (2001) 13.6, 13.4fine sandsscrubland73892300132.73T
12.1, 12.8fine sandsscrubland22.53892184191.23T
Goodrich et al. (2004) 31.8, −110.8silty clayscrubland3324149999.90T
Green et al. (2008) 41.6, −96.6silt loamcropland159720203102494.41WTF
41.6, −96.6loamy sandgrassland48720102494.41WTF
Gregory et al. (1992) −32.1, 117.1sandy loamcropland6.5380146969.55WB
Gupta and Sharma (1984) 22.9, 76.6sandcropland677501690348.93T
22.9, 76.6sandcropland818941690348.93T
22.9, 76.6sandcropland948211690348.93T
Hadas et al. (1999)31.3, 34.6360 mm d−1cropland70210525145257.65WB, T
31.9, 34.8680 mm d−1cropland73.75671311136.85WB, T
32.1, 34.8330 mm d−1cropland81.65442661290141.14WB, T
32.3, 34.9680 mm d−1cropland95.95881501307154.64WB, T
Halm et al. (2002) −7.1, −41.8sandCropland14.57001835163.65WB
−7.1, −41.8sandScrubland6.57001835163.65WB
Hatton and Nulsen (1999) −35.4, 147.6sandy clay loamgrassland3611107938.75model
−35.4, 147.6sandy clay loamgrassland134611107938.75model
−35.4, 147.6sandy clay loamwoodland0611107938.75model
Heilweil et al. (2006) 37.1, −113.3loamscrubland0.3210163934.74T
37.1, −113.3loamscrubland4210163934.74T
37.1, −113.3loamscrubland6.8210163934.74T
37.1, −113.3loamscrubland10210163934.74T
Heng et al. (2001) −35.4, 147.6claygrassland47.6650107938.75WB
Holmes and Colville (1968) −37.8, 140.8sandgrassland120700113286.35lysimeter
Holmes and Colville (1970a) −37.8, 140.8sandgrassland63600113286.35WB
Holmes and Colville (1970b) −37.9, 140.9sandwoodland0600114784.25WB
Holmstead et al. (1988) 29.1, −99.9loamgrassland0273156558.92lysimeter
29.1, −99.9loamgrassland1.2736156558.92lysimeter
29.1, −99.9loamno vegetation10.7273156558.92lysimeter
29.1, −99.9loamno vegetation29.9736156558.92lysimeter
Houston (1982) −14.4, 28.41800 mm d−1no vegetation2819371448240.33base flow
−14.4, 28.41800 mm d−1woodland809371448240.33base flow
Howard and Karundu (1992) 0.1, 30.8loamcropland668691235105.44WB
0.1, 30.8loamgrassland33.58691235105.44WB
0.1, 30.8loamno vegetation818691235105.44WB
0.1, 30.8loamwoodland08691235105.44WB
Huang and Gallichand (2006) 35.3, 107.8silty clay loamcropland18.3545818109.90model
Hughes et al. (1988) −35.1, 140.1sandy loamcropland16.5340133523.15T
Hume (1997) −35.8, 150.1coarse sandswoodland200800116363.81
Hussein (2001) 31.1, 33.8sandno vegetation18300140525.64T
31.1, 33.8sandno vegetation24300140525.64T
Jackson and Rushton (1987) 50.1, 10.1boulder claycropland2452154038.51WB
Jipp et al. (1998) −2.9, −47.6claygrassland287167213213234WB
−2.9, −47.6claywoodland141167213213234WB
−2.9, −47.6claywoodland187167213213234WB
Johnston (1987a) −33.4, 115.9claywoodland28.1122015041785T
−33.4, 115.9claywoodland75122015041785T
Johnston (1987b) −33.3, 116.4claywoodland2.458001423138.95T
−33.4, 115.9sandwoodland26.5125015041785T
Jolly (1992) −32.3, 18.4coarse sandsscrubland23.5196139840.74WTF
Jolly et al. (1989) −35.1, 140.3sandcropland453701346225T
−35.1, 140.3sandwoodland0.83701346225T
Joshi (1997) 52.1, −106.1siltcropland1237169951.30T
52.1, −106.1siltgrassland137169951.30T
Julien et al. (1988) 33.3, −99.3fine sandy loamgrassland0723161070.92lysimeter
33.3, −99.3fine sandy loamgrassland0811161070.92lysimeter
33.3, −99.3fine sandy loamgrassland0852161070.92lysimeter
33.3, −99.3fine sandy loamno vegetation10.8837161070.92lysimeter
33.3, −99.3fine sandy loamwoodland0678161070.92lysimeter
Kendy et al. (2003) 37.9, 114.8loamcropland66.3367811031150.61WB
37.9, 114.8loamcropland1053673011031150.61WB
37.9, 114.8loamcropland1403673711031150.61WB
37.9, 114.8loamcropland1743674601031150.61WB
Kendy et al. (2004) 37.9, 114.8loamcropland2004611031150.61WB
37.9, 114.8loamcropland6904619001031150.61WB
37.9, 114.8loamcropland130046115001031150.61WB
Kennett-Smith et al. (1990) −34.3, 141.3sandy clay loamcropland4310138711.53T, WB
−34.3, 141.3loamy sandcropland7.5310138711.53T, WB
−34.6, 142.8loamy sandcropland13.6312142115.94T, WB
−34.6, 143.6sandy clay loamcropland18322137813.86T, WB
Kennett-Smith et al. (1992a) −37.6, 143.9claycropland3430110856.76T, WB
Kennett-Smith et al. (1992b) −33.4, 142.6loamy sandgrassland0.425514939.43T, WB
Kennett-Smith et al. (1993) −35.8, 141.4claygrassland3.5530129427.85T, WB
Kennett-Smith et al. (1994) −35.1, 141.9sandy clay loamcropland9340137315.75T
Kienzle and Schulze (1992) −27.4, 32.6sandwoodland1798501337103.20WB
Knoche et al. (2002) 51.8, 13.6sandwoodland8265261636.71model
Krajenbrink et al. (1988) 52.3, 5.6coarse sandscropland30585451834.34T
52.3, 5.6coarse sandsgrassland30585451834.34T
52.3, 5.6coarse sandswoodland10185451834.34T
Külls (2000) −24.3, 29.9sandscrubland11.5465134198.61T
Ladekarl et al. (2005) 56.4, 8.9sandscrubland733107745049.43T
56.4, 9.4sandwoodland39087544540.24T
Larsen et al. (2002) −19.9, 28.3sandscrubland2555015281262T
Leaney and Allison (1986) −34.1, 139.9sandwoodland0.15275139413.93T
−34.1, 139.9sandwoodland0.25275139413.93T
Leaney and Herczeg (1995) −36.3, 140.8claycropland1.15451245446T
−36.3, 140.8claycropland105454501245446T
−36.3, 140.8sandcropland605451245446T
−36.3, 140.8claywoodland0.55451245446T
−36.3, 140.8sandwoodland0.55451245446T
Leaney and Herczeg (1999) −35.3, 140.8claygrassland12375640134623.35T
−35.3, 140.9claywoodland0.3440134521.55T
−36.6, 141.3sandwoodland1.5450121646.36T
Leduc et al. (2001) 13.6, 2.6sandscrubland35652162174.93T
13.6, 2.6sandscrubland65652162174.93T
13.6, 2.6sandscrubland205652162174.93WTF
Li et al. (2005) 36.1, 140.1loamgrassland39211947811302WB
Lin and Wei (2001) 42.9, 118.9silt loamno vegetation47360899118.30T
37.8, 113.8silt loamno vegetation68550931146.80T
42.9, 118.9silt loamno vegetation85360899118.30T
37.8, 113.8silt loamno vegetation288550931146.80T
Loh and Stokes (1981) −32.9, 121.6sandcropland15390146223.55T
−32.9, 117.6sandcropland19410133161.45WTF
−31.8, 116.4sandcropland305901570125.44WTF
−33.3, 116.4sandcropland407501423138.95WTF
−33.3, 116.6sandcropland556501396115.75WTF
−33.3, 116.4sandcropland607251423138.95WTF
−33.4, 115.9sandcropland100115015041785WTF
−31.8, 116.4claygrassland245901570125.44WTF
−33.4, 115.9sandwoodland10125015041785WTF
Maréchal et al. (2006) 17.4, 78.4claycropland1146131651704180.54WTF
Maréchal et al. (2009) 11.8, 76.4claywoodland7512731386501.93WTF, T
McDowall et al. (2003) −33.4, 121.9sandgrassland55.3522144843.85WB
McMahon et al. (2003) 37.8, −100.8sandcropland53487675141968.40T
37.3, −101.8loamy fine sandgrassland5.1453146457.72T
McMahon et al. (2006) 33.6, −102.8loamcropland17420585162757.61T
33.8, −102.8loamcropland24.54404501622581T
33.6, −102.8loamcropland32420433162757.61T
33.8, −102.8sandy loamcropland394205931622581T
33.8, −102.8loamy sandcropland544206381622581T
33.8, −102.8sandy loamcropland1024203301622581T
33.8, −102.8sandy loamcropland1114205401622581T
34.1, −102.8loamy sandgrassland0.24201595601T
37.3, −101.8loamy sandgrassland5453146457.72T
40.6, −101.8sandgrassland705001191752T
Mileham et al. (2008) −0.9, 30.1sandy loamcropland1041190112699.54WB
Milroy et al. (2008) −29.6, 115.8sandcropland25.1324196974.14model
−29.6, 115.8sandcropland37.9356190074.14model
−29.6, 115.8sandcropland40.6387180074.14model
−29.6, 115.8sandcropland45339196974.14model
−29.6, 115.8sandcropland54.3409170074.14model
−29.6, 115.8sandcropland83.1461162274.14model
Monirul Islam and Kanungoe (2005) 24.8, 88.6claycropland15314422071195360.32WB
Müller and Bolte (2009) 52.6, 13.4sandgrassland28562059336.61lysimeter
52.6, 13.4sandwoodland74.462059336.61lysimeter
52.6, 13.4sandwoodland80.662059336.61lysimeter
52.6, 13.4sandwoodland12462059336.61lysimeter
Navada et al. (2001)24.9, 71.1fine sandscropland122401872106.42T
24.9, 71.1fine sandscropland14.52401872106.42T
24.9, 71.1fine sandscropland182401872106.42T
25.4, 71.1fine sandscropland20240183681.32T
Newman et al. (1997) 35.8, −106.3loamgrassland1470144448.81T
35.8, −106.3fine sandy loamwoodland0.45510144448.81T
35.8, −106.3loamwoodland0.8470144448.81T
Nichols and Verry (2001) 47.6, −93.4fine sandy loamwoodland10978472589.90WB
O'Connell et al. (2003) −35.1, 141.9sandy loamcropland5.3356137315.75lysimeter
Ojeda (2001) 28.4, −110.8sandscrubland0.11320173792.51T
28.4, −110.8sandscrubland0.16320173792.51T
31.6, −106.9sandscrubland0.24230178039.40T
Pakrou and Dillon (2000) −37.8, 140.8silt loamcropland129750113286.35lysimeter
−37.8, 140.8silt loamcropland163750113286.35lysimeter
Paydar and Gallant (2008) −35.8, 146.8360 mm d−1cropland93.8546107133.15model
−35.8, 146.8360 mm d−1grassland17.6546107133.15model
Peck and Hurle (1973) −32.4, 116.8sandgrassland24490146898.75base flow
−32.8, 116.8sandgrassland26730143495.55base flow
−33.1, 116.9sandgrassland37500137384.45base flow
−33.3, 116.6sandgrassland608201396115.75base flow
−31.8, 116.3sandgrassland618801597149.74base flow
−31.4, 116.1sandgrassland789101647125.74base flow
−32.4, 116.8sandwoodland0.82490146898.75WB
−33.1, 116.9sandwoodland1.2500137384.45WB
−33.3, 116.6sandwoodland1.78201396115.75WB
−32.8, 116.8sandwoodland1.9730143495.55WB
−31.8, 116.3sandwoodland3.98801597149.74WB
−31.4, 116.1sandwoodland89101647125.74WB
−31.6, 116.3sandwoodland13.46601620126.64T
−32.9, 116.3sandwoodland24.211001470181.65T
−33.3, 116.3sandwoodland33.48701456162.25T
−32.8, 116.1sandwoodland10613501517229.15T
−32.3, 116.1sandwoodland13411471546217.74T
−32.3, 116.1sandwoodland15711001559217.74T
Peck et al. (1981) −33.4, 116.1sandwoodland0.6911501457169.75T, model
−33.4, 116.1sandwoodland88001457169.75T, model
−33.4, 116.1sandwoodland10413001457169.75T, model
−33.4, 116.1sandwoodland15011501457169.75T, model
Pracilio et al. (2003) −31.3, 117.6loamy sandcropland123361541455model
−31.3, 117.6loamy sandcropland323361541455model
−31.3, 117.6sandcropland533361541455model
Prych (1998) 46.6, −119.4loamgrassland1.2160108318.55T
46.6, −119.4loamgrassland5.1160108318.55T
46.6, −119.4silt loamscrubland0.06160108318.55T
46.6, −119.4loamscrubland0.15160108318.55T
46.6, −119.4loamscrubland2.6160108318.55T
Radford et al. (2009) −24.8, 150.1claycropland1.6700150272.11T
−23.9, 150.3claycropland2659160093.90T
−23.1, 148.1claycropland7.4597163893.81T
−24.3, 149.8claycropland8.9632154882.81T
−23.9, 148.4claycropland16.1600159694.71T
−22.9, 148.9claycropland18580162196.80T
−24.3, 150.4claycropland27.5639157988.21T
−22.9, 148.9claywoodland0.2580162196.80T
−23.9, 148.4claywoodland0.2600159694.71T
−23.9, 150.3claywoodland0.2659160093.90T
−24.3, 149.8cracking claywoodland0.3632154882.81T
−24.3, 149.8cracking claywoodland0.3638154882.81T
−24.8, 150.1cracking claywoodland0.3700150272.11T
−24.3, 150.4claywoodland0.3639157988.21T
−23.1, 148.1cracking claywoodland1.7597163893.81T
Ragab et al. (1997) 52.3, −2.6loamy sandgrassland6862544425.95WB
52.3, 0.34grassland9155048120.41WB
50.8, −3.3loamy sandgrassland153738477566WB
52.3, 0.34grassland16555048120.41lysimeter
51.1, −1.3silty clay loamgrassland21377146938.95WB
Rangarajan et al. (2009) 8.8, 78.1sandy loamcropland16.35821514186.65T
8.8, 78.1sandcropland47.65821514186.65T
8.8, 78.1sandy loamcropland605821514186.65T
8.8, 78.1claycropland70.25821514186.65T
8.8, 78.1sandcropland82.35821514186.65T
Renard et al. (1993) 31.8, −110.8loamscrubland0.2303149999.90model
Renger and Wessolek (1990) 53.1, 10.8sandcropland23061551932.50
51.4, 9.3deposits of glacial tillcropland23268751634.91
Renger et al. (1986) 52.3, 9.8fine sandscropland225655518341WB, model
52.3, 9.8fine sandsgrassland190655518341WB, model
52.3, 9.8fine sandswoodland110655518341WB, model
Richardson and Narayan (1995) −34.4, 135.9sandcropland40550140866.95WTF, WB
−34.4, 135.9sandgrassland10550140866.95model
Ridley et al. (1997) −36.1, 146.6sandy clay loamgrassland74.5693105642.25WB
−36.1, 146.6sandy clay loamgrassland83693105642.25WB
−36.1, 146.6sandy clay loamno vegetation83693105642.25WB
−36.1, 146.6sandy clay loamno vegetation142693105642.25WB
−36.1, 146.6fine sandy clay loamcropland36.5600105642.25WB
−36.1, 146.6fine sandy clay loamcropland51.5600105642.25model
−36.1, 146.6fine sandy clay loamgrassland5.5600105642.25WB
−36.1, 146.6fine sandy clay loamgrassland6.8600105642.25model
Roberts and Rosier (2006) 51.1, −1.3silty claygrassland20798646938.95WB
51.1, −1.3silty claywoodland300100446938.95WB
Rodvang et al. (2004) 49.9, −112.8fine sandy clay loamcropland11.640030085150.41T
49.9, −112.8coarse sandscropland29.740035085150.41T
49.9, −112.8fine sandy clay loamcropland34.740030085150.41WTF
49.9, −112.8coarse sandscropland59.740035085150.41WTF
49.9, −112.8coarse sandscropland11740035085150.41T
49.9, −112.8coarse sandscropland17040044085150.41T
49.9, −112.8coarse sandsgrassland4240044085150.41T
Sami and Hughes (1996) −32.8, 26.1loamgrassland5.2460134250.72T
−32.8, 26.1loamgrassland5.8460134250.72model
Santoni et al. (2010) −33.6, −65.8sandy loamcropland5.35181317860T
−33.6, −65.8sandy loamcropland6.95021317860T
−33.6, −65.8sandy loamcropland7.95021317860T
−33.8, −65.8sandy loamcropland9.6542129482.70T
−33.4, −65.9sandy loamcropland10.45381383900T
−33.6, −65.8sandy loamcropland10.85181317860T
−33.4, −65.9sandy loamcropland13.25381383900T
−33.8, −65.8sandy loamcropland128542129482.70T
−33.4, −66.6sandy loamwoodland0.02447147684.60T
−33.4, −65.9sandy loamwoodland0.045381383900T
−33.6, −65.8sandy loamwoodland0.055021317860T
−33.6, −65.8sandy loamwoodland0.145181317860T
−33.8, −65.8sandy loamwoodland0.33542129482.70T
Scanlon (1991) 31.4, −105.8silt loamscrubland0.07280176643.71T
Scanlon and Goldsmith (1997) 35.3, −101.8silty clay loamgrassland0.62500157171.51T
Scanlon et al. (1999) 31.1, −105.3claygrassland0.02320173748.41T
31.1, −105.3clay loamgrassland0.05320173748.41T
Scanlon et al. (2005) 32.9, −102.1sandy loamcropland19.5457167053.62T
32.9, −102.1sandcropland24457167053.62T
32.9, −102.1sandgrassland2457167053.62T
36.8, −116.8sandscrubland0.5113187011.35T
Scanlon et al. (2007b) 32.8, −101.9loamy sandcropland19452167753.32T
32.8, −101.9loamy sandcropland31449167753.32T
32.8, −101.9loamy sandcropland39446167753.32T
32.8, −101.9loamy sandgrassland0426167753.32T
Selaolo (1998) −24.1, 25.3sandscrubland8400137288.20T
Selaolo et al. (2003) −23.6, 24.3sandscrubland0.54001433780T
−23.6, 24.3sandscrubland1.14001433780T
−23.6, 24.3sandscrubland3.84001433780T
−24.1, 25.1sandscrubland4420138486.10T
−24.1, 25.1sandscrubland9.8420138486.10T
−25.3, 25.6sandscrubland115001394102.20T
−25.3, 25.6sandscrubland165001394102.20T
Sharda et al. (2006) 23.1, 73.3sandy clay loamcropland62.78351734322.42T
23.1, 73.3sandy clay loamcropland718351734322.42WTF
Sharma and Gupta (1987) 26.3, 73.1sandcropland16.62191963122.82T
26.3, 73.1sandcropland17.42191963122.82T
26.6, 72.8sandno vegetation21.83891907108.61T
26.3, 73.1sandno vegetation22.12191963122.82T
26.8, 71.3sandno vegetation22.3165177065.22T
26.3, 73.1sandno vegetation25.72191963122.82T
26.6, 72.8sandno vegetation46.83891907108.61T
Silburn et al. (2009) −24.8, 149.8cracking claycropland19.8720151075.61T
−24.8, 149.8cracking claygrassland0.16720151075.61T
−24.8, 149.8cracking clayno vegetation32.4720151075.61T
−24.8, 149.8cracking claywoodland0.17720151075.61T
−24.8, 149.8claywoodland0.26720151075.61T
Singh et al. (1984) −0.1, 34.8sandy clay loamgrassland5512781702163.92WB
Sloots and Wijnen (1990) −24.4, 25.6sandscrubland9492135794.10
Smettem (1998) −33.9, 121.8sandgrassland35500141085.55WB
Smith et al. (1998) −35.4, 147.6sandy clay loamcropland33.3343107938.75WB, lysimeter
−35.4, 147.6sandy clay loamcropland97628107938.75WB, lysimeter
Snow et al. (1999) −35.4, 147.6850 mm d−1woodland216674896107938.75model
Sophocleous (2005) 34.3, −102.8clay loamcropland7408159659.51model
38.9, −101.8clay loamcropland15465127469.82model
40.6, −102.3clay loamcropland29.5448120174.62model
40.6, −102.3silt loamcropland49.5448120174.62model
38.1, −101.3clay loamcropland91623137665.91model
47.9, −97.1clay loamcropland10246480967.11model
40.6, −98.1clay loamcropland109668113895.52model
34.3, −102.8clay loamgrassland0408159659.51model
40.6, −102.3clay loamgrassland1448120174.62model
40.6, −102.3silt loamgrassland2448120174.62model
38.9, −101.8clay loamgrassland8465127469.82model
38.1, −101.3clay loamgrassland19623137665.91model
47.9, −97.1clay loamgrassland4046480967.11model
40.6, −98.1clay loamgrassland92668113895.52model
Sophocleous and McAllister (1987) 38.1, −98.8silty clay loamcropland65600133087.91WB
38.1, −98.8coarse sandscropland103600133087.91WB
38.1, −98.8silty clay loamgrassland1.6600133087.91WB
38.1, −98.8coarse sandsgrassland42600133087.91WB
Stone et al. (1983) 46.6, −119.4sandno vegetation127240108318.55
Stonestrom et al. (2003) 38.6, −116.1sandscrubland011313589.34T
Sukhija et al. (1988) 11.9, 79.8coarse sandscropland8012001702290.76T
11.9, 79.8sandcropland11012001702290.76T
11.9, 79.8coarse sandscropland13012001702290.76T
11.9, 79.8sandcropland16012001702290.76T
11.9, 79.8sandcropland18012001702290.76T
11.9, 79.8coarse sandscropland20012001702290.76T
Sumioka and Bauer (2004) 48.3, −122.6sandy loamwoodland89.861864369.14T
48.3, −122.6coarse sandswoodland11661864369.14WB
Sun and Cornish (2005) −31.8, 150.6180 mm d−1grassland4.9738122665.30model
Talsma and Gardner (1986) −35.4, 148.810 mm d−1woodland1201230102059.25base flow, WTF
Taylor and Howard (1996) 2.6, 32.6claycropland20014001558174.66T, model
Thorburn et al. (1991) −24.8, 149.8claycropland17.6650151075.61T
−24.8, 149.8claygrassland2.3650151075.61T
−24.8, 149.8claywoodland0650151075.61T
Thorpe (1989)−31.8, 115.9sandscrubland1748301661155.54T
Timmerman (1985) −32.3, 18.4coarse sandsscrubland38.7216139840.74
−32.3, 18.4coarse sandsscrubland43.5290139840.74
Timmerman (1986) −32.1, 18.6coarse sandsscrubland20250143444.34
Tomasella et al. (2008) −3.1, −60.1claywoodland43826271299260.85base flow
Unkovich et al. (2003) −35.1, 139.3sandcropland1300130622.65WB
−35.1, 141.9sandy loamcropland2.5330137315.75WB
−36.6, 143.9silty clay loamcropland58.84251241236WB
−36.6, 143.9silty clay loamwoodland0.024251241236WB
van Lanen and Dijksma (1999) 51.1, 5.8sandgrassland29390554724.91model
Vandoolaeghe and Bertram (1982) −33.6, 18.4coarse sandsscrubland98.8380123673.25WB
Vegter (1995) −32.3, 18.4coarse sandsscrubland15.7196139840.74
−32.1, 18.4coarse sandsscrubland23.5196139839.44
Verhagen (1994) −22.1, 26.3sandscrubland6500140883.80T
−23.8, 25.1sandscrubland6450139681.10T
−23.8, 25.1sandscrubland11.5450139681.10T
Walker et al. (1990a) −34.3, 141.3sandy clay loamgrassland4.7295138711.53T
Walker et al. (1990b) −36.8, 140.9claygrassland1520121052.35T
−36.3, 140.8claygrassland55001245446T
−36.9, 140.8claygrassland8.5580120763.45T
Walker et al. (1992a) −35.1, 139.4sandgrassland60580129923.75T
Walker et al. (1992b) −35.4, 139.6sandcropland25.53801278355T
−35.4, 139.6sandy loamgrassland133801278355T
Walvoord and Phillips (2004) 31.4, −104.4clay loamgrassland0.1365169958.22T
31.4, −104.4clay loamscrubland0275169958.22T
31.4, −104.4clay loamscrubland0.05365169958.22T
Wang et al. (2004) 37.4, 104.9fine sandno vegetation48191879541WB, lysimeter
Wang et al. (2008) 37.8, 115.8claycropland15.34232511044154.70T
37.9, 115.8claycropland1316672811041158.90T
38.1, 114.4silty claycropland1686261781019155.11T
37.4, 116.3silty claycropland1986506310791840T
38.3, 116.8siltcropland2566707871059206.30T
37.8, 115.8claygrassland05441044154.70T
37.4, 116.3silty claygrassland84.664310791840T
Wanke et al. (2008) −22.6, 18.335 mm d−1grassland7.64091542761model
−22.6, 18.343 mm d−1no vegetation75.34091542761model
−22.6, 18.3sandscrubland74091542761model
Ward et al. (2002) −33.8, 117.4loamy sandgrassland17483128665.85WB
−33.8, 117.4loamy sandgrassland45483128665.85WB
Watson et al. (2004) −43.6, 172.1silt loamwoodland17625686285lysimeter
Weaver et al. (2005) −30.3, 149.3claycropland31.3514350149563.30T
−30.3, 149.4claycropland56.5460300147965.40T
−30.3, 149.6claycropland72.5417150146671.90T
−30.3, 149.3claycropland87.3514500149563.30T
−30.3, 149.3claycropland121514650149563.30T
Webb et al. (2008) 39.1, −75.4silty loamcropland1591150102032.21model
Wechsung et al. (2000) 52.6, 13.4sandcropland11453459336.61model
52.6, 13.4sandwoodland28.953459336.61model
Wegehenkel et al. (2008) 52.4, 13.3sandgrassland26954560138.21lysimeter
Weltz and Blackburn (1995) 27.6, −98.3fine sandy loamgrassland228871493101.11lysimeter
27.6, −98.3fine sandy loamno vegetation788871493101.11lysimeter
27.6, −98.3fine sandy loamwoodland08871493101.11WB
White (1997)−35.4, 147.6sandgrassland22650107938.75WB
−35.4, 147.6sandgrassland62697107938.75WB
White et al. (2003) −35.1, 147.4sandy clay loamgrassland44.5593113422.14WB
−30.6, 150.6clay loamgrassland47.5662127571.50WB
−37.4, 141.9sandy loamgrassland142642116057.86WB
−33.6, 149.1sandy loamgrassland159885113632.85WB
−34.9, 117.8sandgrassland16175811901035WB
−37.1, 145.9loamy sandgrassland161813106684.76WB
Williamson et al. (2004) 34.3, −117.8sandy loamgrassland55678126768.26T
34.3, −117.8sandy loamscrubland39678126768.26T
Wright et al. (1988) 33.3, −99.3clay loamgrassland0.13679161070.92lysimeter
Zeppel et al. (2006) −31.4, 150.8sandwoodland21752122655.90WB
Zhang et al. (1999) −33.4, 145.6sandy claycropland8.5564140014.73model
−35.1, 142.1sandy clay loamgrassland9.5351137915.55model
Zhu (2000) 36.1, −111.3fine sandsgrassland163051545191T
36.1, −111.3fine sandsgrassland163051545191T
Zouari et al. (2001) 34.9, 8.1sandgrassland0.994122630.84T
ReferenceLat., long.Soil texture or KsVegetationRechargePrecipitationIrrigationPET§Amplitude§Phase§Methods
mm yr−1mm mo−1mo
Abdalla (2008) 11.1, 32.6sandscrubland0.94001930154.24model
11.1, 29.1clayscrubland410251893147.44model
16.1, 34.6sandscrubland7.3130210272.92model
Ahmed and Umar (2008) 29.4, 77.3claycropland2056685501454267.22WTF
29.4, 77.3claycropland2806688001454267.22WTF
29.4, 77.3claycropland3006688001454267.22WTF
Allen (1981) −31.8, 115.9sandscrubland857751661155.54WB
Allison and Hughes (1972) −37.8, 140.8sandgrassland63686113286.35T
−37.8, 140.8sandwoodland13686113286.35T
Allison and Hughes (1978) −37.8, 140.8sandgrassland106700113286.35T
−37.8, 140.8sandgrassland114700113286.35T
Allison and Hughes (1983) −35.1, 142.1sandcropland3.5335137915.55T
−35.1, 142.1sandwoodland0.07335137915.55T
Allison et al. (1985) −34.3, 139.6sandwoodland0.135300137414.83T
Allison et al. (1990) −36.3, 140.8claycropland25001245446T
−36.3, 140.8claycropland25001245446T
−34.3, 139.6sandcropland13300137414.83T
−35.1, 140.3sandy loamcropland25.23701346225T
−34.3, 139.6sandwoodland0.05300137414.83T
−35.1, 140.1sandwoodland0.05340133523.15T
−35.1, 141.9sandwoodland0.06340137315.75T
−35.1, 140.3sandwoodland0.073701346225T
−34.4, 140.1sandy loamwoodland0.07270136115.26WB
−35.1, 140.3sandwoodland0.643701346225WB
−34.4, 140.1sandwoodland1.3270136115.26T
Al-Sagaby andMoallim (2001)25.8, 42.9sandno vegetation1.81332283414T
Amro et al. (2001) 29.8, 35.3sandno vegetation0.03651768105T
32.1, 36.1sandy siltno vegetation0.267150451.65T
32.1, 36.1sandy siltno vegetation1.567150451.65T
32.3, 35.9sandno vegetation8480144777.35T
32.3, 35.9sandno vegetation28480144777.35T
Anderson et al. (1998) −30.6, 116.1loamcropland214703174075.24WB
Andrews et al. (1997) 52.3, 0.4sandcropland8347448120.14WTF
52.3, 0.3clay loamcropland10445548120.41WTF
Anuraga et al. (2006) 13.1, 78.3claycropland8490215301604model
12.9, 78.3claycropland909024101529160.94model
13.1, 78.3claycropland12490215015301604model
13.1, 78.3sandy loamcropland18490215301604model
13.1, 78.3sandy loamcropland22090241015301604model
13.1, 78.3sandy loamcropland23290215015301604model
Athavale et al. (1980) 16.9, 78.6claycropland6711001669177.34T
16.9, 78.6claycropland7311501669177.34T
16.9, 78.6claycropland809701669177.34T
16.9, 78.6sandy clay loamcropland8313101669177.34T
16.9, 78.6sandy loamcropland8311501669177.34T
16.9, 78.6claycropland96.813101669177.34T
16.9, 78.6sandy loamcropland9812001669177.34T
16.9, 78.6sandy loamcropland13313101669177.34T
16.9, 78.6sandy loamcropland22214301669177.34T
Babiker et al. (2005) 35.4, 136.9sandcropland8601915895195.12WB
Beekman et al. (1996) −22.1, 26.3sandscrubland12.5500140883.80T
Bekele et al. (2006) −29.8, 115.6sandcropland14.74401869109.54T
−29.8, 115.6sandcropland35.74401869109.54WTF
−29.8, 115.6sandgrassland16.24401612109.54T
−29.8, 115.6sandgrassland35.94401612109.54T
−29.8, 115.6sandscrubland94401869109.54T
Bellot et al. (1999) 38.3, −0.6loamgrassland61.54541136552model
38.3, −0.6loamno vegetation1254541136552model
38.3, −0.6loamscrubland18.64541136552model
38.3, −0.6loamwoodland9.64541136552model
Bent (2001) 42.4, −72.3fine sandy loamwoodland262124885522.54model
42.4, −72.3fine sandy loamwoodland371116985522.54model
Beverly et al. (2005) −37.3, 144.9sandgrassland113651112140.36model
Bird et al. (2004) −37.8, 142.1clay loamcropland366951149576WB
−37.8, 142.1clay loamgrassland186951149576WB
Bredenkamp and Vandoolaeghe (1982) −33.6, 18.4coarse sandsscrubland73.5350123673.25model
−33.6, 18.4coarse sandsscrubland95350123673.25WB
Butler and Verhagen (2001) −27.1, 22.8sandgrassland1.8337157265.62T
−27.1, 22.8sandgrassland13337157265.62T
Calder et al. (2003) 53.3, −1.1sandgrassland16980044914.65model
53.3, −1.1sandscrubland15680044914.65model
53.3, −1.1sandwoodland3064344914.65T
53.3, −1.1sandwoodland45.864344914.65model
53.3, −1.1sandwoodland6964344914.65WB
53.3, −1.1sandwoodland10664344914.65model
53.3, −1.1sandwoodland12064344914.65WB
Carbon et al. (1982) −31.8, 115.9coarse sandsgrassland1738001661155.54WB
−31.8, 115.9coarse sandswoodland1219001661155.54WB, WTF, T
Carlson et al. (1988) 33.3, −99.3clay loamgrassland7671161070.92lysimeter
33.3, −99.3clay loamno vegetation9.3671161070.92lysimeter
33.3, −99.3clay loamwoodland3.3671161070.92lysimeter
Cherkauer and Ansari (2005) 43.3, −88.3sandcropland123103083971.21base flow
Cho et al. (2009) 37.3, −80.112 mm d−1woodland27104598233.60model
Colville and Holmes (1972) −37.6, 140.8sandgrassland82700115186.95WTF
−37.6, 140.8sandwoodland44700115186.95WTF
Conrad et al. (2005) −32.4, 18.8coarse sandscropland15275144058.74T
−32.4, 18.8coarse sandsscrubland2200144058.74T
Cook (1992) −35.1, 140.1loamy sandcropland9.8340133523.15T
Cook and Kilty (1992) −35.1, 140.1sandcropland9340133523.15EMI
Cook et al. (1989) −34.6, 142.8sandy clay loamcropland7312142115.94T
−34.6, 143.6sandy clay loamcropland8.3322137813.86T
−35.1, 140.1sandgrassland2.7340133523.15EMI
−35.1, 140.1sandgrassland17.4340133523.15T
−35.1, 140.1sandwoodland0.05340133523.15T
Cook et al. (1992a) −34.4, 140.1sandy loamcropland3270136115.26T
Cook et al. (1992b) 15.6, −16.3sandcropland153561853130.12T
Cook et al. (1994) −34.3, 139.6sandgrassland11340137414.83T
−35.1, 140.1sandgrassland13340133523.15T
−35.1, 140.1sandgrassland16340133523.15T
−35.1, 140.1sandwoodland0.1260133523.15T
−35.1, 140.1sandwoodland0.9260133523.15T
Cook et al. (1998) −12.6, 131.1claywoodland200172019313722T
Cook et al. (2004) −34.3, 140.6sandgrassland2.72601373143T
−34.3, 140.6sandgrassland4.92601373143model
−34.3, 140.6sandswoodland0.12601373143T
Crosbie et al. (2007) −34.6, 148.8claygrassland5.2613115325.33WTF
−34.6, 148.8claygrassland48.4613115325.33WTF
Dams et al. (2008) 51.3, 4.8sandcropland29283957726.94model
Daniel (1999) 35.6, −98.1loamcropland93.874314241012WTF
35.6, −98.1loamgrassland63.974314241012WTF
Datta et al. (1980) 23.6, 73.3sandy loamcropland348521724307.72T
23.1, 72.6sandy loamcropland35.66481718276.12T
23.1, 72.6sandy loamcropland58.510141718276.12T
23.4, 72.4sandy loamcropland70.913571754256.52T
23.8, 73.1sandy loamcropland8711451758301.12T
23.4, 72.4sandy loamcropland14416821754256.52T
23.1, 73.1sandy loamcropland18414111731325.62T
De Vries et al. (2000) −24.8, 25.3sandscrubland0.9325137699.60T
−24.8, 25.3sandscrubland1350137699.60T
−24.1, 25.3sandscrubland3420137288.20T
−23.8, 25.1sandscrubland5450139681.10T
Deans et al. (2005) 15.6, −16.3sandcropland153561853130.12T
Di and Cameron (2002) −43.8, 171.8silt loamcropland37065068121.95
Dolling et al. (2007) −29.9, 116.6sandcropland30335173253.15model
−33.9, 117.1sandcropland1154961295775model
Dripps and Bradbury (2007) 43.1, −89.6silt loamcropland25683482475.61WB
43.1, −89.6silt loamcropland29083482475.61WB
46.1, −89.8claygrassland27979068884.91WTF
46.1, −89.8claygrassland28779068884.91WB
46.1, −89.8claywoodland13079068884.91WTF
46.1, −89.8claywoodland17579068884.91WB
46.1, −89.8claywoodland17679068884.91WTF
46.1, −89.8claywoodland26879068884.91WB
Duffková (2002) 49.3, 14.8sandy loamgrassland20.652859952.51lysimeter
Dunin et al. (1999) −35.4, 147.6850 mm d−1cropland15611107938.75WB
−35.4, 147.6850 mm d−1cropland84611107938.75WB
−35.4, 147.6850 mm d−1cropland89611107938.75WB
−35.4, 147.6sandy clay loamcropland185611107938.75WB
−35.4, 147.6sandy clay loamgrassland25611107938.75WB
−35.4, 147.6850 mm d−1grassland2611107938.75WB
Dyck et al. (2003)51.9, −107.3silty loamcropland332171948.70T
Edmunds (2001b) 34.8, 32.9sandgrassland52.54201364104.14T
34.8, 32.9sandgrassland55.54201364104.14T
Edmunds and Gaye (1994) 15.9, −16.3claycropland2.692901858107.31T
15.8, −16.3sandcropland14.92901853118.42T
Edmunds et al. (2002) 13.1, 10.1sandno vegetation35.33142286168.43T
Facchi et al. (2005) 45.1, 9.6coarse sandsgrassland49180051267855.93model
Favreau et al. (2009) 13.6, 2.8sandcropland255572160171.63WTF
13.6, 2.8sandscrubland25572160171.63model
Favreau et al. (2002) 13.4, 2.8sandcropland355672152175.13WTF
13.4, 2.8sandscrubland35672152175.13T
Fayer et al. (1996) 46.6, −119.4loamy sandgrassland1.2159108318.55T
46.6, −119.4sandy loamgrassland5.1159108318.55T
46.6, −119.4sandy loamgrassland25.4159108318.55WB
46.6, −119.4coarse sandsno vegetation55.4159108318.55lysimeter
46.6, −119.4gravelno vegetation86.7184108318.55lysimeter
46.6, −119.4gravelno vegetation300480108318.55lysimeter
46.6, −119.4loamy sandscrubland0.02159108318.55T
46.6, −119.4silt loamscrubland0.05159108318.55T
46.6, −119.4loamy sandscrubland2159108318.55T
46.6, −119.4silt loamscrubland2.75159108318.55T
Fillery and Poulter (2006) −30.8, 116.6loamy sandcropland53495164362.75WB
Finch (1998) 51.6, −1.1sandy clay loamcropland29058747325.35WB
51.6, −1.1sandy clay loamgrassland17658747325.35WB
51.6, −1.1sandy clay loamwoodland9658747325.35WB
Fisher and Healy (2008) 46.3, −119.9silty claycropland119187744106822.55lysimeter, WB
39.3, −76.1fine sandy loamcropland315981104527.61lysimeter, WB
37.3, −120.8sandcropland4232701200138448.46lysimeter, WB
39.8, −85.8silty clay loamcropland47590695568.40lysimeter, WB
Fouty (1989) 36.9, −116.8loamy sandscrubland0.23104175412.25T
Gates et al. (2008) 39.9, 101.9sandgrassland1.584101017.51T
Gaye and Edmunds (1996) 15.8, −16.4sandcropland242901843118.32T
15.8, −16.4sandcropland31.52901843118.32T
Gee et al. (1994) 32.6, −106.4loamy fine sandno vegetation87338170450.81lysimeter, WB
Gee et al. (1993) 46.6, −119.4sandno vegetation71.1172108318.55lysimeter, WB
46.6, −119.4sandno vegetation300480108318.55lysimeter, WB
George and Frantom (1988) −31.6, 118.3sandy claywoodland0.13281501425T
−31.6, 118.3sandy claywoodland1.53281501425T
Gieske (1992) −24.4, 25.6sandscrubland10492135794.10T
Gieske et al. (1995)−24.3, 25.3sandscrubland9425137291.60T
−24.3, 25.3sandscrubland15425137291.60T
Goni and Edmunds (2001) 13.6, 13.4fine sandsscrubland73892300132.73T
12.1, 12.8fine sandsscrubland22.53892184191.23T
Goodrich et al. (2004) 31.8, −110.8silty clayscrubland3324149999.90T
Green et al. (2008) 41.6, −96.6silt loamcropland159720203102494.41WTF
41.6, −96.6loamy sandgrassland48720102494.41WTF
Gregory et al. (1992) −32.1, 117.1sandy loamcropland6.5380146969.55WB
Gupta and Sharma (1984) 22.9, 76.6sandcropland677501690348.93T
22.9, 76.6sandcropland818941690348.93T
22.9, 76.6sandcropland948211690348.93T
Hadas et al. (1999)31.3, 34.6360 mm d−1cropland70210525145257.65WB, T
31.9, 34.8680 mm d−1cropland73.75671311136.85WB, T
32.1, 34.8330 mm d−1cropland81.65442661290141.14WB, T
32.3, 34.9680 mm d−1cropland95.95881501307154.64WB, T
Halm et al. (2002) −7.1, −41.8sandCropland14.57001835163.65WB
−7.1, −41.8sandScrubland6.57001835163.65WB
Hatton and Nulsen (1999) −35.4, 147.6sandy clay loamgrassland3611107938.75model
−35.4, 147.6sandy clay loamgrassland134611107938.75model
−35.4, 147.6sandy clay loamwoodland0611107938.75model
Heilweil et al. (2006) 37.1, −113.3loamscrubland0.3210163934.74T
37.1, −113.3loamscrubland4210163934.74T
37.1, −113.3loamscrubland6.8210163934.74T
37.1, −113.3loamscrubland10210163934.74T
Heng et al. (2001) −35.4, 147.6claygrassland47.6650107938.75WB
Holmes and Colville (1968) −37.8, 140.8sandgrassland120700113286.35lysimeter
Holmes and Colville (1970a) −37.8, 140.8sandgrassland63600113286.35WB
Holmes and Colville (1970b) −37.9, 140.9sandwoodland0600114784.25WB
Holmstead et al. (1988) 29.1, −99.9loamgrassland0273156558.92lysimeter
29.1, −99.9loamgrassland1.2736156558.92lysimeter
29.1, −99.9loamno vegetation10.7273156558.92lysimeter
29.1, −99.9loamno vegetation29.9736156558.92lysimeter
Houston (1982) −14.4, 28.41800 mm d−1no vegetation2819371448240.33base flow
−14.4, 28.41800 mm d−1woodland809371448240.33base flow
Howard and Karundu (1992) 0.1, 30.8loamcropland668691235105.44WB
0.1, 30.8loamgrassland33.58691235105.44WB
0.1, 30.8loamno vegetation818691235105.44WB
0.1, 30.8loamwoodland08691235105.44WB
Huang and Gallichand (2006) 35.3, 107.8silty clay loamcropland18.3545818109.90model
Hughes et al. (1988) −35.1, 140.1sandy loamcropland16.5340133523.15T
Hume (1997) −35.8, 150.1coarse sandswoodland200800116363.81
Hussein (2001) 31.1, 33.8sandno vegetation18300140525.64T
31.1, 33.8sandno vegetation24300140525.64T
Jackson and Rushton (1987) 50.1, 10.1boulder claycropland2452154038.51WB
Jipp et al. (1998) −2.9, −47.6claygrassland287167213213234WB
−2.9, −47.6claywoodland141167213213234WB
−2.9, −47.6claywoodland187167213213234WB
Johnston (1987a) −33.4, 115.9claywoodland28.1122015041785T
−33.4, 115.9claywoodland75122015041785T
Johnston (1987b) −33.3, 116.4claywoodland2.458001423138.95T
−33.4, 115.9sandwoodland26.5125015041785T
Jolly (1992) −32.3, 18.4coarse sandsscrubland23.5196139840.74WTF
Jolly et al. (1989) −35.1, 140.3sandcropland453701346225T
−35.1, 140.3sandwoodland0.83701346225T
Joshi (1997) 52.1, −106.1siltcropland1237169951.30T
52.1, −106.1siltgrassland137169951.30T
Julien et al. (1988) 33.3, −99.3fine sandy loamgrassland0723161070.92lysimeter
33.3, −99.3fine sandy loamgrassland0811161070.92lysimeter
33.3, −99.3fine sandy loamgrassland0852161070.92lysimeter
33.3, −99.3fine sandy loamno vegetation10.8837161070.92lysimeter
33.3, −99.3fine sandy loamwoodland0678161070.92lysimeter
Kendy et al. (2003) 37.9, 114.8loamcropland66.3367811031150.61WB
37.9, 114.8loamcropland1053673011031150.61WB
37.9, 114.8loamcropland1403673711031150.61WB
37.9, 114.8loamcropland1743674601031150.61WB
Kendy et al. (2004) 37.9, 114.8loamcropland2004611031150.61WB
37.9, 114.8loamcropland6904619001031150.61WB
37.9, 114.8loamcropland130046115001031150.61WB
Kennett-Smith et al. (1990) −34.3, 141.3sandy clay loamcropland4310138711.53T, WB
−34.3, 141.3loamy sandcropland7.5310138711.53T, WB
−34.6, 142.8loamy sandcropland13.6312142115.94T, WB
−34.6, 143.6sandy clay loamcropland18322137813.86T, WB
Kennett-Smith et al. (1992a) −37.6, 143.9claycropland3430110856.76T, WB
Kennett-Smith et al. (1992b) −33.4, 142.6loamy sandgrassland0.425514939.43T, WB
Kennett-Smith et al. (1993) −35.8, 141.4claygrassland3.5530129427.85T, WB
Kennett-Smith et al. (1994) −35.1, 141.9sandy clay loamcropland9340137315.75T
Kienzle and Schulze (1992) −27.4, 32.6sandwoodland1798501337103.20WB
Knoche et al. (2002) 51.8, 13.6sandwoodland8265261636.71model
Krajenbrink et al. (1988) 52.3, 5.6coarse sandscropland30585451834.34T
52.3, 5.6coarse sandsgrassland30585451834.34T
52.3, 5.6coarse sandswoodland10185451834.34T
Külls (2000) −24.3, 29.9sandscrubland11.5465134198.61T
Ladekarl et al. (2005) 56.4, 8.9sandscrubland733107745049.43T
56.4, 9.4sandwoodland39087544540.24T
Larsen et al. (2002) −19.9, 28.3sandscrubland2555015281262T
Leaney and Allison (1986) −34.1, 139.9sandwoodland0.15275139413.93T
−34.1, 139.9sandwoodland0.25275139413.93T
Leaney and Herczeg (1995) −36.3, 140.8claycropland1.15451245446T
−36.3, 140.8claycropland105454501245446T
−36.3, 140.8sandcropland605451245446T
−36.3, 140.8claywoodland0.55451245446T
−36.3, 140.8sandwoodland0.55451245446T
Leaney and Herczeg (1999) −35.3, 140.8claygrassland12375640134623.35T
−35.3, 140.9claywoodland0.3440134521.55T
−36.6, 141.3sandwoodland1.5450121646.36T
Leduc et al. (2001) 13.6, 2.6sandscrubland35652162174.93T
13.6, 2.6sandscrubland65652162174.93T
13.6, 2.6sandscrubland205652162174.93WTF
Li et al. (2005) 36.1, 140.1loamgrassland39211947811302WB
Lin and Wei (2001) 42.9, 118.9silt loamno vegetation47360899118.30T
37.8, 113.8silt loamno vegetation68550931146.80T
42.9, 118.9silt loamno vegetation85360899118.30T
37.8, 113.8silt loamno vegetation288550931146.80T
Loh and Stokes (1981) −32.9, 121.6sandcropland15390146223.55T
−32.9, 117.6sandcropland19410133161.45WTF
−31.8, 116.4sandcropland305901570125.44WTF
−33.3, 116.4sandcropland407501423138.95WTF
−33.3, 116.6sandcropland556501396115.75WTF
−33.3, 116.4sandcropland607251423138.95WTF
−33.4, 115.9sandcropland100115015041785WTF
−31.8, 116.4claygrassland245901570125.44WTF
−33.4, 115.9sandwoodland10125015041785WTF
Maréchal et al. (2006) 17.4, 78.4claycropland1146131651704180.54WTF
Maréchal et al. (2009) 11.8, 76.4claywoodland7512731386501.93WTF, T
McDowall et al. (2003) −33.4, 121.9sandgrassland55.3522144843.85WB
McMahon et al. (2003) 37.8, −100.8sandcropland53487675141968.40T
37.3, −101.8loamy fine sandgrassland5.1453146457.72T
McMahon et al. (2006) 33.6, −102.8loamcropland17420585162757.61T
33.8, −102.8loamcropland24.54404501622581T
33.6, −102.8loamcropland32420433162757.61T
33.8, −102.8sandy loamcropland394205931622581T
33.8, −102.8loamy sandcropland544206381622581T
33.8, −102.8sandy loamcropland1024203301622581T
33.8, −102.8sandy loamcropland1114205401622581T
34.1, −102.8loamy sandgrassland0.24201595601T
37.3, −101.8loamy sandgrassland5453146457.72T
40.6, −101.8sandgrassland705001191752T
Mileham et al. (2008) −0.9, 30.1sandy loamcropland1041190112699.54WB
Milroy et al. (2008) −29.6, 115.8sandcropland25.1324196974.14model
−29.6, 115.8sandcropland37.9356190074.14model
−29.6, 115.8sandcropland40.6387180074.14model
−29.6, 115.8sandcropland45339196974.14model
−29.6, 115.8sandcropland54.3409170074.14model
−29.6, 115.8sandcropland83.1461162274.14model
Monirul Islam and Kanungoe (2005) 24.8, 88.6claycropland15314422071195360.32WB
Müller and Bolte (2009) 52.6, 13.4sandgrassland28562059336.61lysimeter
52.6, 13.4sandwoodland74.462059336.61lysimeter
52.6, 13.4sandwoodland80.662059336.61lysimeter
52.6, 13.4sandwoodland12462059336.61lysimeter
Navada et al. (2001)24.9, 71.1fine sandscropland122401872106.42T
24.9, 71.1fine sandscropland14.52401872106.42T
24.9, 71.1fine sandscropland182401872106.42T
25.4, 71.1fine sandscropland20240183681.32T
Newman et al. (1997) 35.8, −106.3loamgrassland1470144448.81T
35.8, −106.3fine sandy loamwoodland0.45510144448.81T
35.8, −106.3loamwoodland0.8470144448.81T
Nichols and Verry (2001) 47.6, −93.4fine sandy loamwoodland10978472589.90WB
O'Connell et al. (2003) −35.1, 141.9sandy loamcropland5.3356137315.75lysimeter
Ojeda (2001) 28.4, −110.8sandscrubland0.11320173792.51T
28.4, −110.8sandscrubland0.16320173792.51T
31.6, −106.9sandscrubland0.24230178039.40T
Pakrou and Dillon (2000) −37.8, 140.8silt loamcropland129750113286.35lysimeter
−37.8, 140.8silt loamcropland163750113286.35lysimeter
Paydar and Gallant (2008) −35.8, 146.8360 mm d−1cropland93.8546107133.15model
−35.8, 146.8360 mm d−1grassland17.6546107133.15model
Peck and Hurle (1973) −32.4, 116.8sandgrassland24490146898.75base flow
−32.8, 116.8sandgrassland26730143495.55base flow
−33.1, 116.9sandgrassland37500137384.45base flow
−33.3, 116.6sandgrassland608201396115.75base flow
−31.8, 116.3sandgrassland618801597149.74base flow
−31.4, 116.1sandgrassland789101647125.74base flow
−32.4, 116.8sandwoodland0.82490146898.75WB
−33.1, 116.9sandwoodland1.2500137384.45WB
−33.3, 116.6sandwoodland1.78201396115.75WB
−32.8, 116.8sandwoodland1.9730143495.55WB
−31.8, 116.3sandwoodland3.98801597149.74WB
−31.4, 116.1sandwoodland89101647125.74WB
−31.6, 116.3sandwoodland13.46601620126.64T
−32.9, 116.3sandwoodland24.211001470181.65T
−33.3, 116.3sandwoodland33.48701456162.25T
−32.8, 116.1sandwoodland10613501517229.15T
−32.3, 116.1sandwoodland13411471546217.74T
−32.3, 116.1sandwoodland15711001559217.74T
Peck et al. (1981) −33.4, 116.1sandwoodland0.6911501457169.75T, model
−33.4, 116.1sandwoodland88001457169.75T, model
−33.4, 116.1sandwoodland10413001457169.75T, model
−33.4, 116.1sandwoodland15011501457169.75T, model
Pracilio et al. (2003) −31.3, 117.6loamy sandcropland123361541455model
−31.3, 117.6loamy sandcropland323361541455model
−31.3, 117.6sandcropland533361541455model
Prych (1998) 46.6, −119.4loamgrassland1.2160108318.55T
46.6, −119.4loamgrassland5.1160108318.55T
46.6, −119.4silt loamscrubland0.06160108318.55T
46.6, −119.4loamscrubland0.15160108318.55T
46.6, −119.4loamscrubland2.6160108318.55T
Radford et al. (2009) −24.8, 150.1claycropland1.6700150272.11T
−23.9, 150.3claycropland2659160093.90T
−23.1, 148.1claycropland7.4597163893.81T
−24.3, 149.8claycropland8.9632154882.81T
−23.9, 148.4claycropland16.1600159694.71T
−22.9, 148.9claycropland18580162196.80T
−24.3, 150.4claycropland27.5639157988.21T
−22.9, 148.9claywoodland0.2580162196.80T
−23.9, 148.4claywoodland0.2600159694.71T
−23.9, 150.3claywoodland0.2659160093.90T
−24.3, 149.8cracking claywoodland0.3632154882.81T
−24.3, 149.8cracking claywoodland0.3638154882.81T
−24.8, 150.1cracking claywoodland0.3700150272.11T
−24.3, 150.4claywoodland0.3639157988.21T
−23.1, 148.1cracking claywoodland1.7597163893.81T
Ragab et al. (1997) 52.3, −2.6loamy sandgrassland6862544425.95WB
52.3, 0.34grassland9155048120.41WB
50.8, −3.3loamy sandgrassland153738477566WB
52.3, 0.34grassland16555048120.41lysimeter
51.1, −1.3silty clay loamgrassland21377146938.95WB
Rangarajan et al. (2009) 8.8, 78.1sandy loamcropland16.35821514186.65T
8.8, 78.1sandcropland47.65821514186.65T
8.8, 78.1sandy loamcropland605821514186.65T
8.8, 78.1claycropland70.25821514186.65T
8.8, 78.1sandcropland82.35821514186.65T
Renard et al. (1993) 31.8, −110.8loamscrubland0.2303149999.90model
Renger and Wessolek (1990) 53.1, 10.8sandcropland23061551932.50
51.4, 9.3deposits of glacial tillcropland23268751634.91
Renger et al. (1986) 52.3, 9.8fine sandscropland225655518341WB, model
52.3, 9.8fine sandsgrassland190655518341WB, model
52.3, 9.8fine sandswoodland110655518341WB, model
Richardson and Narayan (1995) −34.4, 135.9sandcropland40550140866.95WTF, WB
−34.4, 135.9sandgrassland10550140866.95model
Ridley et al. (1997) −36.1, 146.6sandy clay loamgrassland74.5693105642.25WB
−36.1, 146.6sandy clay loamgrassland83693105642.25WB
−36.1, 146.6sandy clay loamno vegetation83693105642.25WB
−36.1, 146.6sandy clay loamno vegetation142693105642.25WB
−36.1, 146.6fine sandy clay loamcropland36.5600105642.25WB
−36.1, 146.6fine sandy clay loamcropland51.5600105642.25model
−36.1, 146.6fine sandy clay loamgrassland5.5600105642.25WB
−36.1, 146.6fine sandy clay loamgrassland6.8600105642.25model
Roberts and Rosier (2006) 51.1, −1.3silty claygrassland20798646938.95WB
51.1, −1.3silty claywoodland300100446938.95WB
Rodvang et al. (2004) 49.9, −112.8fine sandy clay loamcropland11.640030085150.41T
49.9, −112.8coarse sandscropland29.740035085150.41T
49.9, −112.8fine sandy clay loamcropland34.740030085150.41WTF
49.9, −112.8coarse sandscropland59.740035085150.41WTF
49.9, −112.8coarse sandscropland11740035085150.41T
49.9, −112.8coarse sandscropland17040044085150.41T
49.9, −112.8coarse sandsgrassland4240044085150.41T
Sami and Hughes (1996) −32.8, 26.1loamgrassland5.2460134250.72T
−32.8, 26.1loamgrassland5.8460134250.72model
Santoni et al. (2010) −33.6, −65.8sandy loamcropland5.35181317860T
−33.6, −65.8sandy loamcropland6.95021317860T
−33.6, −65.8sandy loamcropland7.95021317860T
−33.8, −65.8sandy loamcropland9.6542129482.70T
−33.4, −65.9sandy loamcropland10.45381383900T
−33.6, −65.8sandy loamcropland10.85181317860T
−33.4, −65.9sandy loamcropland13.25381383900T
−33.8, −65.8sandy loamcropland128542129482.70T
−33.4, −66.6sandy loamwoodland0.02447147684.60T
−33.4, −65.9sandy loamwoodland0.045381383900T
−33.6, −65.8sandy loamwoodland0.055021317860T
−33.6, −65.8sandy loamwoodland0.145181317860T
−33.8, −65.8sandy loamwoodland0.33542129482.70T
Scanlon (1991) 31.4, −105.8silt loamscrubland0.07280176643.71T
Scanlon and Goldsmith (1997) 35.3, −101.8silty clay loamgrassland0.62500157171.51T
Scanlon et al. (1999) 31.1, −105.3claygrassland0.02320173748.41T
31.1, −105.3clay loamgrassland0.05320173748.41T
Scanlon et al. (2005) 32.9, −102.1sandy loamcropland19.5457167053.62T
32.9, −102.1sandcropland24457167053.62T
32.9, −102.1sandgrassland2457167053.62T
36.8, −116.8sandscrubland0.5113187011.35T
Scanlon et al. (2007b) 32.8, −101.9loamy sandcropland19452167753.32T
32.8, −101.9loamy sandcropland31449167753.32T
32.8, −101.9loamy sandcropland39446167753.32T
32.8, −101.9loamy sandgrassland0426167753.32T
Selaolo (1998) −24.1, 25.3sandscrubland8400137288.20T
Selaolo et al. (2003) −23.6, 24.3sandscrubland0.54001433780T
−23.6, 24.3sandscrubland1.14001433780T
−23.6, 24.3sandscrubland3.84001433780T
−24.1, 25.1sandscrubland4420138486.10T
−24.1, 25.1sandscrubland9.8420138486.10T
−25.3, 25.6sandscrubland115001394102.20T
−25.3, 25.6sandscrubland165001394102.20T
Sharda et al. (2006) 23.1, 73.3sandy clay loamcropland62.78351734322.42T
23.1, 73.3sandy clay loamcropland718351734322.42WTF
Sharma and Gupta (1987) 26.3, 73.1sandcropland16.62191963122.82T
26.3, 73.1sandcropland17.42191963122.82T
26.6, 72.8sandno vegetation21.83891907108.61T
26.3, 73.1sandno vegetation22.12191963122.82T
26.8, 71.3sandno vegetation22.3165177065.22T
26.3, 73.1sandno vegetation25.72191963122.82T
26.6, 72.8sandno vegetation46.83891907108.61T
Silburn et al. (2009) −24.8, 149.8cracking claycropland19.8720151075.61T
−24.8, 149.8cracking claygrassland0.16720151075.61T
−24.8, 149.8cracking clayno vegetation32.4720151075.61T
−24.8, 149.8cracking claywoodland0.17720151075.61T
−24.8, 149.8claywoodland0.26720151075.61T
Singh et al. (1984) −0.1, 34.8sandy clay loamgrassland5512781702163.92WB
Sloots and Wijnen (1990) −24.4, 25.6sandscrubland9492135794.10
Smettem (1998) −33.9, 121.8sandgrassland35500141085.55WB
Smith et al. (1998) −35.4, 147.6sandy clay loamcropland33.3343107938.75WB, lysimeter
−35.4, 147.6sandy clay loamcropland97628107938.75WB, lysimeter
Snow et al. (1999) −35.4, 147.6850 mm d−1woodland216674896107938.75model
Sophocleous (2005) 34.3, −102.8clay loamcropland7408159659.51model
38.9, −101.8clay loamcropland15465127469.82model
40.6, −102.3clay loamcropland29.5448120174.62model
40.6, −102.3silt loamcropland49.5448120174.62model
38.1, −101.3clay loamcropland91623137665.91model
47.9, −97.1clay loamcropland10246480967.11model
40.6, −98.1clay loamcropland109668113895.52model
34.3, −102.8clay loamgrassland0408159659.51model
40.6, −102.3clay loamgrassland1448120174.62model
40.6, −102.3silt loamgrassland2448120174.62model
38.9, −101.8clay loamgrassland8465127469.82model
38.1, −101.3clay loamgrassland19623137665.91model
47.9, −97.1clay loamgrassland4046480967.11model
40.6, −98.1clay loamgrassland92668113895.52model
Sophocleous and McAllister (1987) 38.1, −98.8silty clay loamcropland65600133087.91WB
38.1, −98.8coarse sandscropland103600133087.91WB
38.1, −98.8silty clay loamgrassland1.6600133087.91WB
38.1, −98.8coarse sandsgrassland42600133087.91WB
Stone et al. (1983) 46.6, −119.4sandno vegetation127240108318.55
Stonestrom et al. (2003) 38.6, −116.1sandscrubland011313589.34T
Sukhija et al. (1988) 11.9, 79.8coarse sandscropland8012001702290.76T
11.9, 79.8sandcropland11012001702290.76T
11.9, 79.8coarse sandscropland13012001702290.76T
11.9, 79.8sandcropland16012001702290.76T
11.9, 79.8sandcropland18012001702290.76T
11.9, 79.8coarse sandscropland20012001702290.76T
Sumioka and Bauer (2004) 48.3, −122.6sandy loamwoodland89.861864369.14T
48.3, −122.6coarse sandswoodland11661864369.14WB
Sun and Cornish (2005) −31.8, 150.6180 mm d−1grassland4.9738122665.30model
Talsma and Gardner (1986) −35.4, 148.810 mm d−1woodland1201230102059.25base flow, WTF
Taylor and Howard (1996) 2.6, 32.6claycropland20014001558174.66T, model
Thorburn et al. (1991) −24.8, 149.8claycropland17.6650151075.61T
−24.8, 149.8claygrassland2.3650151075.61T
−24.8, 149.8claywoodland0650151075.61T
Thorpe (1989)−31.8, 115.9sandscrubland1748301661155.54T
Timmerman (1985) −32.3, 18.4coarse sandsscrubland38.7216139840.74
−32.3, 18.4coarse sandsscrubland43.5290139840.74
Timmerman (1986) −32.1, 18.6coarse sandsscrubland20250143444.34
Tomasella et al. (2008) −3.1, −60.1claywoodland43826271299260.85base flow
Unkovich et al. (2003) −35.1, 139.3sandcropland1300130622.65WB
−35.1, 141.9sandy loamcropland2.5330137315.75WB
−36.6, 143.9silty clay loamcropland58.84251241236WB
−36.6, 143.9silty clay loamwoodland0.024251241236WB
van Lanen and Dijksma (1999) 51.1, 5.8sandgrassland29390554724.91model
Vandoolaeghe and Bertram (1982) −33.6, 18.4coarse sandsscrubland98.8380123673.25WB
Vegter (1995) −32.3, 18.4coarse sandsscrubland15.7196139840.74
−32.1, 18.4coarse sandsscrubland23.5196139839.44
Verhagen (1994) −22.1, 26.3sandscrubland6500140883.80T
−23.8, 25.1sandscrubland6450139681.10T
−23.8, 25.1sandscrubland11.5450139681.10T
Walker et al. (1990a) −34.3, 141.3sandy clay loamgrassland4.7295138711.53T
Walker et al. (1990b) −36.8, 140.9claygrassland1520121052.35T
−36.3, 140.8claygrassland55001245446T
−36.9, 140.8claygrassland8.5580120763.45T
Walker et al. (1992a) −35.1, 139.4sandgrassland60580129923.75T
Walker et al. (1992b) −35.4, 139.6sandcropland25.53801278355T
−35.4, 139.6sandy loamgrassland133801278355T
Walvoord and Phillips (2004) 31.4, −104.4clay loamgrassland0.1365169958.22T
31.4, −104.4clay loamscrubland0275169958.22T
31.4, −104.4clay loamscrubland0.05365169958.22T
Wang et al. (2004) 37.4, 104.9fine sandno vegetation48191879541WB, lysimeter
Wang et al. (2008) 37.8, 115.8claycropland15.34232511044154.70T
37.9, 115.8claycropland1316672811041158.90T
38.1, 114.4silty claycropland1686261781019155.11T
37.4, 116.3silty claycropland1986506310791840T
38.3, 116.8siltcropland2566707871059206.30T
37.8, 115.8claygrassland05441044154.70T
37.4, 116.3silty claygrassland84.664310791840T
Wanke et al. (2008) −22.6, 18.335 mm d−1grassland7.64091542761model
−22.6, 18.343 mm d−1no vegetation75.34091542761model
−22.6, 18.3sandscrubland74091542761model
Ward et al. (2002) −33.8, 117.4loamy sandgrassland17483128665.85WB
−33.8, 117.4loamy sandgrassland45483128665.85WB
Watson et al. (2004) −43.6, 172.1silt loamwoodland17625686285lysimeter
Weaver et al. (2005) −30.3, 149.3claycropland31.3514350149563.30T
−30.3, 149.4claycropland56.5460300147965.40T
−30.3, 149.6claycropland72.5417150146671.90T
−30.3, 149.3claycropland87.3514500149563.30T
−30.3, 149.3claycropland121514650149563.30T
Webb et al. (2008) 39.1, −75.4silty loamcropland1591150102032.21model
Wechsung et al. (2000) 52.6, 13.4sandcropland11453459336.61model
52.6, 13.4sandwoodland28.953459336.61model
Wegehenkel et al. (2008) 52.4, 13.3sandgrassland26954560138.21lysimeter
Weltz and Blackburn (1995) 27.6, −98.3fine sandy loamgrassland228871493101.11lysimeter
27.6, −98.3fine sandy loamno vegetation788871493101.11lysimeter
27.6, −98.3fine sandy loamwoodland08871493101.11WB
White (1997)−35.4, 147.6sandgrassland22650107938.75WB
−35.4, 147.6sandgrassland62697107938.75WB
White et al. (2003) −35.1, 147.4sandy clay loamgrassland44.5593113422.14WB
−30.6, 150.6clay loamgrassland47.5662127571.50WB
−37.4, 141.9sandy loamgrassland142642116057.86WB
−33.6, 149.1sandy loamgrassland159885113632.85WB
−34.9, 117.8sandgrassland16175811901035WB
−37.1, 145.9loamy sandgrassland161813106684.76WB
Williamson et al. (2004) 34.3, −117.8sandy loamgrassland55678126768.26T
34.3, −117.8sandy loamscrubland39678126768.26T
Wright et al. (1988) 33.3, −99.3clay loamgrassland0.13679161070.92lysimeter
Zeppel et al. (2006) −31.4, 150.8sandwoodland21752122655.90WB
Zhang et al. (1999) −33.4, 145.6sandy claycropland8.5564140014.73model
−35.1, 142.1sandy clay loamgrassland9.5351137915.55model
Zhu (2000) 36.1, −111.3fine sandsgrassland163051545191T
36.1, −111.3fine sandsgrassland163051545191T
Zouari et al. (2001) 34.9, 8.1sandgrassland0.994122630.84T

Approximate latitude and longitude of the studies.

Values as reported in the studies.

§

Estimated from the Climate Research Unit data set: PET, potential evapotranspiration; amplitude, difference between maximum and minimum mean monthly precipitation; phase, number of months between maximum mean monthly precipitation and temperature.

T, natural and injected tracers such as Cl− and stable and radio isotopes of water; WTF, water table fluctuations; WB, water balance from monitoring of soil moisture or evapotranspiration; base flow, base flow of surface water bodies; model, simulations of soil water movement, water balance, geographic information systems, or spatially explicit models; EMI, electromagnetic induction.

This work was supported by the National Science Foundation (DEB no. 0717191, IOS no. 0920355, and GRFP no. 2006044266). We wish to thank members of the Jackson lab for helpful comments on the manuscript, Nancy Scott and Chinling Chen for their assistance with the database, and Ricardo Andres, Matt Cleary, Laura Beth Konopinski, and others for their outstanding help in the lab and the field. We also thank many landowners and personnel at the following research centers who provided access to the sites and logistical support: Grupo Estudios Ambientales in San Luis, Argentina; Institute for Agricultural Plant Physiology and Ecology at Universidad de Buenos Aires (UBA); Estancia San Claudio maintained by UBA; Western Kansas Agricultural Research Center in Tribune; Oklahoma Panhandle Research and Extension Center in Goodwell; Texas AgriLife research and extension centers at Vernon and San Angelo; and USDA-ARS Grassland Soil and Water Research Laboratory in Temple, TX.

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