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"SPAW（Soil Plant Atmosphere Water）软件与ArcGIS辅助工具是一套强大的环境科学计算工具，主要用于土壤、水文和气象数据的分析与管理。这个RAR压缩包包含的资源显然是为了帮助用户在ArcGIS环境下更好地进行相关研究和工作。 SPAW软件是一款专门针对农业、水资源管理和环境保护领域的计算软件。它能够模拟土壤-植物-大气间的水分交换过程，为科学家和工程师提供准确的水文模型。这款软件通常用于评估农田灌溉、水分蒸发、作物生长条件等方面，对于农业规划、水资源管理以及气候变化影响研究具有重要意义。在SPAW软件中，用户可以输入土壤类型、气候数据、作物参数等信息，软件将通过复杂的算法计算出土壤水分状况、作物需水量、蒸散发量等关键指标。这些计算结果有助于优化灌溉策略，提高农作物产量，同时也有助于水资源的合理分配和保护。 ArcGIS则是Esri公司开发的一款强大的地理信息系统（GIS），广泛应用于地图制作、空间分析和数据管理等领域。当SPAW与ArcGIS结合使用时，用户可以利用ArcGIS的可视化和空间分析功能，对SPAW计算出的水文数据进行直观展示和深度挖掘。例如，可以通过ArcGIS创建地图，显示不同区域的土壤湿度变化，或者分析气候变化对作物生长的影响。在提供的"spaw_arcgis辅助"文件中，可能包含了一系列脚本、工具或教程，帮助用户将SPAW模型集成到ArcGIS环境中。这些辅助工具可能包括自定义 ArcGIS 工具箱，使得用户可以直接在ArcGIS界面内运行SPAW模型，简化操作流程；也可能是GIS数据集，用于测试模型或演示如何结合GIS数据使用SPAW；还可能包含详细的使用指南和案例研究，帮助用户理解和应用这些工具。在实际应用中，这样的组合可以极大地提升水文和环境研究的效率，使专业人员能够快速评估和预测各种环境条件下的水分动态，从而做出更科学的决策。无论是农业研究人员，还是水资源管理者，都能够从这套工具中受益，提升其工作的精确性和实用性。 "SPAW软件_arcgis辅助用.rar"压缩包提供的资源是一个集成的解决方案，旨在利用GIS的强大功能增强SPAW软件的使用体验，为处理土壤、水文和气象问题的专家提供了一个高效且直观的工作平台。通过深入学习和熟练掌握这两款工具的结合，用户能够在复杂的数据海洋中找到关键的环境信息，为保护和管理我们的自然资源提供有力支持。"

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SPAW Hydrology Setup (6.02.75).exe 8.85MB

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Soil Water Characteristics-Equations.xls 262KB

Soil Water Characteristics-Paper.pdf 1.47MB

Soil Water Characteristic Estimates by Texture and Organic Matter for

Hydrologic Solutions

K. E. Saxton and W. J. Rawls

ABSTRACT

Hydrologic analyses often involve the evaluation of soil water in-

filtration, conductivity, storage, and plant-water relationships. To de-

fine the hydrologic soil water effects requires estimating soil water

characteristics for water potential and hydraulic conductivity using soil

variables such as texture, organic matter (OM), and structure. Field or

laboratory measurements are difficult, costly, and often impractical for

many hydrologic analyses. Statistical correlations between soil texture,

soil water potential, and hydraulic conductivity can provide estimates

sufficiently accurate for many analyses and decisions. This study de-

veloped new soil water characteristic equations from the currently

available USDA soil database using only the readily available vari-

ables of soil texture and OM. These equations are similar to those

previously reported by Saxton et al. but include more variables and

application range. They were combined with previously reported rela-

tionships for tensions and conductivities and the effects of density,

gravel, and salinity to form a comprehensive predictive system of soil

water characteristics for agricultural water management and hydro-

logic analyses. Verification was performed using independent data

sets for a wide range of soil textures. The predictive system was pro-

grammed for a graphical computerized model to provide easy appli-

cation and rapid solutions and is available at http://hydrolab.arsusda.

gov/soilwater/Index.htm.

YDROLOGIC ANALYSES are commonly achieved by

computer simulation of individual processes, then

combined into more comprehensive results and ana-

lyzed by statistics or time series. This contrasts with

earlier methodology, which relied heavily on statistical

analyses of measured hydrologic data. While modern

methods do not ignore available data, simulation of the

individual processes and recombination into landscape

and watershed responses often reveals additional details

beyond that previously available, particularly where

data are limited or not available.

A significant percentage of most precipitation infil-

trates to become stored soil water, which is either re-

turned to the atmosphere by plant transpiration and

evaporation or is conducted to lower levels and ground

water. As a result, modern simulation and analyses of

hydrologic processes relies heavily on appropriate de-

scriptions of the soil water holding and transmission char-

acteristics of the soil profile.

Soil science research has developed an extensive un-

derstanding of soil water and its variability with soil

characteristics (Van Genuchten and Leij, 1992). Appli-

cation of this knowledge is imperative for hydro-

logic simulation within natural landscapes. However,

hydrologists often do not have the capability or time to

perform field or laboratory determinations. Estimated

values can be determined from local soil maps and

published water retention and saturated conductivity

estimates, but these methods often do not provide

sufficient range or accuracy for computerized hydro-

logic analyses.

The texture based method reported by Saxton et al.

(1986), largely based on the data set and analyses of

Rawls et al. (1982), has been successfully applied to a

wide variety of analyses, particularly those of agricul-

tural hydrology and water management, for example,

SPAW model (Saxton and Willey, 1999, 2004, 2006).

Other methods have provided similar results but with

limited versatility (Williams et al., 1992; Rawls et al.,

1992; Stolte et al., 1994). Recent results of pedotrans-

fer functions (Pachepsky and Rawls, 2005) are an exam-

ple of modern equations that cannot be readily applied

because the input requirements are beyond that cus-

tomarily available for hydrologic analyses. Currently

available estimating methods have proven difficult to

assemble and apply over a broad range of soil types and

moisture regimes. Therefore, the objectives of this study

were to (1) update the Saxton et al. (1986) soil water ten-

sion equations with new equations derived from a large

USDA soils database using only commonly available

variables of soil texture and OM, (2) incorporate the

improved conductivity equation of Rawls et al. (1998),

and (3) combine these with the effects of bulk density,

gravel, and salinity to provide a broadly applicable pre-

dictive system.

LITERATURE REVIEW

Estimating soil water hydraulic characteristics from

readily available physical parameters has been a long-

term goal of soil physicists and engineers. Several equa-

tions commonly applied to hydrologic analyses were

summarized by Rawls et al. (1992; Table 5.1.1) and Hillel

(1998). These included those developed by Campbell

(1974), Brooks and Corey (1964), Van Genuchten (1980)

and others. Many early trials were sufficiently success-

ful with limited data sets to suggest that there were sig-

nificant underlying relationships between soil water

characteristics and parameters such as soil textur e

(Gupta and Larson, 1979; Arya and Paris, 1981; Williams

et al., 1983; Ahuja et al., 1985, 1999; Rawls et al., 1998;

Gijsman et al., 2002). More recent studies have eval-

uated additional variables and relationships (Vereecken

et al., 1989; Van Genuchten and Leij, 1992; Pachepsky

and Rawls, 2005).

K.E. Saxton, Saxton Engineering and Associates, 1250 SW Campus

View, Pullman WA 99163; W.J. Rawls, USDA-ARS Hydrology and

Remote Sensing Lab, Bldg. 007, Rm. 104, BARC-W, Beltsville, MD

20705. Received 8 Apr. 2005. *Corresponding author (wrawls@

hydrolab.arsusda.gov).

Published in Soil Sci. Soc. Am. J. 70:1569–1578 (2006).

Soil & Water Management & Conservation, Soil Physics

doi:10.2136/sssaj2005.0117

ª Soil Science Society of America

677 S. Segoe Rd., Madison, WI 53711 USA

Reproduced from Soil Science Society of America Journal. Published by Soil Science Society of America. All copyrights reserved.

1569

Several estimating methods developed in recent years

have shown that generalized predictions can be made

with usable, but variable, accuracy (Rawls et al., 1982;

Saxton et al., 1986; Williams et al., 1992; Stolte et al.,

1994; Kern, 1995). Nearly all of these methods involve

multiple soil descriptors, some of which are often not

available for practical applications. Most were derived

by statistical correlations, although more recent analyses

have explored neural network analysis (Schaap et al.,

1998) or field descriptions and pedotransfer functions

(Grossman et al., 2001; Rawls and Pachepsky, 2002).

Gijsman et al. (2002) reported an extensive review of

eight modern estimating methods applicable to hydro-

logic and agronomic analyses. They observed significant

discrepancy among the methods due to the regional data

basis or methods of analyses thus creating doubt on the

value of lab-measured water retention data for crop

models. They concluded that…“ an analysis with a set of

field-measured data showed that the method of Saxton

et al. (1986) performed the best….” Thus an enhance-

ment of the Saxton et al. (1986) method is an appropriate

extension to improve the field applications of soil water

characteristic estimates with improved data basis and

supplemented by recently derived relationships of con-

ductivity and including appropriate local adjustments

for OM, density, gravel, and salinity.

METHODOLOGY

An extensive laboratory data set of soil water char-

acteristics was obtained from the USDA/NRCS Na-

tional Soil Characterization database (Soil Survey Staff,

2004) consisting of approximately 2000 A-horizon and

2000 B-C horizon samples (B-C a subset of 6700). The

data for each sample included soil water content at 33-

and 1500-kPa tensions; bulk densities; sand (S), silt and

clay (C) particle sizes; and OM. These data were devel-

oped with standard laboratory procedures (USDA-SCS,

1982; Klute, 1986) with reviews and approval for consis-

tency and accuracy.

The B-C horizon data had much less average OM

content than that of the A horizon, 0.6 vs. 2.8% (w)

respectively. Preliminary correlations showed that com-

bining B-C horizon samples with those of the A-horizon

significantly masked the effect of OM. Because texture

and OM are primary variables affecting soil water

content, only the A-horizon data were used to develop

regression equations.

Samples with “extreme” values were omitted from the

data. Excluded were those with bulk density , 1.0 and

. 1.8 g cm

,OM. 8 % (w) and clay . 60% (w). This

reduced the A-horizon data set from 2149 to 1722

samples. Samples outside the density range may have

been the result of tillage or compaction causing them to

be unlike natural soils. The high OM samples were con-

sidered from an “organic” soil whose water character-

istics would not be representative of typical mineral

soils. Soils of very high clay content often have pore

structure and mineralogical effects different than those

containing higher portions of S or silt fractions.

The soil water retention data were correlated with

variables of S, C, and OM and their interactions (Hahn,

1982, p. 218). Density was not included as a correla-

tion variable because it was highly variable within the

A-horizon data set and is not commonly available for

applications. Regression equations were developed for

moisture held at tensions of 1500, 33, 0 to 33 kPa, and

air-entry tensions. Air-entry values were estimated from

the sample data by the exponential form of the Campbell

equation (Rawls et al., 1992, Table 5.1.1). Saturation

moisture (u

) values were estimated from the reported

sample bulk densities assuming particle density of

2.65 g cm

Standard regression methods minimize the statistical

error about a model equation. However, the best equa-

tion form is often unknown and may not adequately

represent the data, thus does not provide a satisfactory

predictive equation. Multi-variable linear analyses are

particularly suspect in this regard because one or more

of the variables may not be linearly correlated with

the dependent variables. This “lack-of-fit” was partially

compensated for by applying a second correlation to the

prediction deviations by the first correlation resulting in

two combined dependent equations, the second being

linear or nonlinear.

Finally, the new moisture tension equations were com-

bined with the conductivity equations of Rawls et al.

(1998) and additional equations for density, gravel, and

salinity effects. The results of the derived correlation

equations were compared with three independent data

sets representative of a wide range of soils to verify their

capability for field applications. A companion com-

puter model and graphical interface of the equations

provides rapid computations and displays for hydro-

logic applications.

PREDICTION EQUATIONS

New predictive equations to estimate soil water con-

tent at selected tensions of 1500, 33, 0 to 33, and c

kPa

are summarized in Table 1 (Eq. [1]–[4])

. Variable de-

fini tions are shown i n Table 2. The coefficient of

determination (R

) and standard error of estimate (S

)

define the data representation and expected predic-

tive accuracy.

Moisture at the selected tensions was correlated with

S, C, and OM plus interactions while air-entry tension

(bubbling pressure), c

, was correlated with S, C, and

S-33

plus interactions. Supplemental analyses of the

initial predictive errors provided “lack of fit” second-

ary adjustment equations for each equation as defined

in methodology.

Graphical results of the correlations are shown in

Fig. 1 for soil moisture and air entry. The best moisture

correlation was obtained for u

1500

5 0.86) with

progressively more variability for u

5 0.63) and

%w indicates decimal percent by weight basis, and %v indicates

decimal percent by volume basis.

Equations throughout the text are referenced to those in Table 1.

Reproduced from Soil Science Society of America Journal. Published by Soil Science Society of America. All copyrights reserved.

1570 SOIL SCI. SOC. AM. J., VOL. 70, SEPTEMBER–OCTOBER 2006

(S-33)

5 0.36). Air-entry pressures, c

, were reason-

ably well estimated (R

5 0.74).

Preliminary correlations for u

with both A- and BC-

horizon data showed poor results (R

, 0.25). These

values were based on reported sample densities which

were likely subject to factors such as tillage, compaction

or roots and worms, which are not related to the cor-

relation variables of texture and OM (Rawls, 1983;

Grossman et al., 2001). As shown in Fig. 1C, the u

(S-33)

correlation was slightly better (R

5 0.36) than for u

thus u

equations were developed as the combination of

those for u

(S-33)

, plus a small S adjustment derived

by an error analyses (Eq. [5]).

A normal (average) density (r

) can be computed

from the estimated u

assuming a particle density of 2.65

(Eq. [6]). To accommodate local variations of soil den-

sity by structure or management, a density adjustment

factor (DF) with a range of 0.9 to 1.3 was incorporated

to estimate values of r

, u

S-DF

, u

33-DF

, and u

(S-33)DF

(Eq. [7]–[10]).

To form a full-range computational scheme, the

moisture-tension relationship was represented by three

tension segments of 1500–33, 33-c

, and c

–0, kPa. The

1500- to 33-kPa range was estimated by an exponential

equation (Eq. [11]) with A and B parameters developed

from the logarithmic form using estimated values u

1500

and u

(Eq. [14]–[15]). The 33-c

kPa segment was

assumed linear (Eq. [12]) based on common experience

that the exponential form often poorly represents these

low tensions and a linear segment is an acceptable sub-

stitute for most applications. The c

–0 range was set at

a constant moisture of u

(Eq. [13]). Example moisture-

tension relationships using Eq. [11] through [15] are

shown in Fig. 2.

Saturated (K

) and unsaturated (K

) conductivity

equations (Eq. [16]–[17]) were adapted from those of

Rawls et al. (1998) and Campbell (1974). The K

equation,

of the form suggested by Kozeny–Carman (Carman,

1956) and Ahuja et al. (1984), is a power function of

moisture held at low tensions within the larger pores

which most effectively conduct water. The value of l

(Eq. [18]) is the inverse of the exponential tension-

moisture curve slope B (Eq. [15]).

Several published equations have represented K

estimate the decrease of water conductivity as soil water

becomes less than saturation (Brooks and Corey, 1964;

Table 2. Equation symbol definitions.

Symbol Definition

A, B Coefficients of moisture-tension, Eq. [11]

C Clay, %w

DF Density adjustment Factor (0.9–1.3)

EC Electrical conductance of a saturated soil extract,

dS m

(dS/m 5 mili-mho cm

)

FC Field Capacity moisture (33 kPa), %v

OM Organic Matter, %w

PAW Plant Avail. moisture (33–1500 kPa, matric soil), %v

PA W

Plant Avail. moisture (33–1500 kPa, bulk soil), %v

S Sand, %w

SAT Saturation moisture (0 kPa), %v

WP Wilting point moisture (1500 kPa), %v

Moisture at tension Y,%v

1500t

1500 kPa moisture, first solution, %v

1500

1500 kPa moisture, %v

33t

33 kPa moisture, first solution, %v

33 kPa moisture, normal density, %v

33-DF

33 kPa moisture, adjusted density, %v

(S-33)t

SAT-33 kPa moisture, first solution, %v

(S-33)

SAT-33 kPa moisture, normal density %v

(S-33)DF

SAT-33 kPa moisture, adjusted density, %v

Saturated moisture (0 kPa), normal density, %v

S-DF

Saturated moisture (0 kPa), adjusted density, %v

Tension at moisture u,kPa

Tension at air entry, first solution, kPa

Tension at air entry (bubbling pressure), kPa

Saturated conductivity (matric soil), mm h

Saturated conductivity (bulk soil), mm h

Unsaturated conductivity at moisture u,mmh

Normal density, g cm

Bulk soil density (matric plus gravel), g cm

Adjusted density, g cm

l Slope of logarithmic tension-moisture curve

a Matric soil density/gravel density (2.65) 5 r/2.65

Rv Volume fraction of gravel (decimal), g cm

Rw Weight fraction of gravel (decimal), g g

Osmotic potential at u 5 u

,kPa

Osmotic potential at u , u

,kPa

Table 1. Equation summary for soil water characteristic estimates.†

Variable Equation R

Eq.

Moisture Regressions

1500

5 u

1500t

1 (0:14 3 u

1500t

2 0:02) 0.86/0.02 1

1500t

520:024S 1 0:487C 1 0:006OM

1 0:005(S 3 OM) 2 0:013(C 3 OM)

1 0:068(S 3 C) 1 0:031

5 u

33t

1 [1:283(u

33t

)

2 0:374(u

33t

) 2 0:015] 0.63/0.05 2

33t

520:251S 1 0:195C 1 0:011OM

1 0:006(S 3 OM) 2 0:027(C 3 OM)

1 0:452(S 3 C) 1 0:299

(S-33)

S233

5 u

(S233)t

1 (0:636u

(S233)t

2 0:107)

0.36/0.06 3

(S233)t

5 0:278S 1 0:034C 1 0:022OM

2 0:018(S 3 OM) 2 0:027(C 3OM)

2 0:584(S 3 C) 1 0:078

5 Y

1 (0:02Y

2 0:113Y

2 0:70) 0.78/2.9 4

5221:67S 2 27:93C 2 81:97u

S233

1 71:12(S 3 u

S233

) 1 8:29(C 3 u

S233

)

1 14:05(S 3 C) 1 27:16

5 u

1 u

(S233)

2 0:097S 1 0:043 0.29/0.04 5

5 (1 2 u

)2:65 6

Density Effects

5 r

3 DF

S-DF

S2DF

5 1 2 (r

=2:65) 8

33-DF

332DF

5 u

2 0:2(u

2 u

S2DF

) 9

(S-33)DF

(S233)DF

5 u

S2DF

2 u

332DF

Moisture-Tension

(1500-33)

5 A(u)

(33-Ye)

5 33:0 2 [(u 2 u

)(33:0 2 Y

)=(u

2 u

)]

(Ye-0)

u 5 u

A 5 exp(ln33 1 Blnu

)

B 5 [ln(1500) 2 ln(33)]=[ln(u

) 2 ln(u

1500

)]

Moisture–Conductivity

5 1930(u

2 u

)

(32l)

5 K

(u=u

)

[3 1 (2=l)]

ll5 1=B 18

Gravel Effects

5 (aR

)=[1 2 R

(1 2 a)]

5 r

(1 2 R

) 1 (R

3 2:65) 20

PA W

PAW

5 PAW(1 2 R

) 21

1 2 R

[1 2 R

(1 2 3a=2)]

Salinity Effects

5 36EC

(36EC) 24

† All symbols defined in Table 2.

Reproduced from Soil Science Society of America Journal. Published by Soil Science Society of America. All copyrights reserved.

1571SAXTON & RAWLS: SOIL WATER CHARACTERISTICS ESTIMATES

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