Simulation of land use–soil interactive effects on water and sediment yields at watershed scale

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Simulation of land use–soil interactive effects on water and sediment yields at watershed scale Xixi Wang a,∗ , Shiyou Shang b , Wanhong Yang c , Calvin R. Clary a , Dawen Yang d a

Hydrology and Watershed Management Program, Department of Engineering & Physics, Tarleton State University, Box T–0390, Stephenville, TX 76402, USA b College of Mechanical and Electrical Engineering, Inner Mongolia Agriculture University, Hohhot, Inner Mongolia 010018, China c Department of Geography, University of Guelph, Guelph, Ontario, Canada N1G 2W1 d Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, PR China

a r t i c l e

i n f o

a b s t r a c t

Article history:

Influences of vegetation management on soil erosion have been extensively studied. How-

Received 27 October 2008

ever, interactive effects between land use and soil are poorly documented in literature. Given

Received in revised form

the importance of understanding such effects for successful watershed management, the

23 November 2008

objective of this study was to examine the land use–soil interactive effects on water and sed-

Accepted 25 November 2008

iment yields for the 117,845-ha drainage area upstream of the U.S. Geological Survey flow gauging station 08101000 in the Cowhouse Creek watershed located in north central Texas. The examination was implemented using the Soil and Water Assessment Tool (SWAT), a

Keywords:

distributed watershed model that has been widely used to tackle problems relevant to non-

BMPs

point source pollution. A SWAT model was calibrated and validated in accordance with the

Brush control

observed daily streamflows at this gauging station. Subsequently, the calibrated model was

Sediment

used to examine changes of water and sediment yields as a result of the conversion of

Soil

range brush to range grasses on an individual soil basis. The results indicated that for the

SWAT

study area, the removal of range brush would result in an annual water yield increase of

Watershed

24 mm ha−1 treated area. However, the removal on an upland soil with a moderately high

Water supply

permeability was predicted to increase the annual water yield by 80 mm ha−1 treated area, while it would result in a small increase of annual sediment loading (4.2 t ha−1 treated area) and a minimal alteration to the existing spatial patterns of sediment sources. The increase of water yield would be larger for the removal of range brush on a soil that is adjacent to the stream channels. For a given soil, the predicted water yield increase was greater for the wetter hydrologic condition than that for the drier one. A reasonable generalization of this study was that the development of best management practices for watershed health and sustainability may need to take into account land use–soil interactive effects on an individual soil basis. © 2008 Elsevier B.V. All rights reserved.

1.

Introduction

Water is the most valuable, but also the most limited, natural resource (TSSWCB, 2003). Limitation of water is likely to

∗

Corresponding author. Tel.: +1 254 968 9164; fax: +1 254 968 9503. E-mail address: [email protected] (X. Wang). 0925-8574/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.ecoleng.2008.11.011

become more apparent due to the pollution from a variety of nonpoint source constituents (Bosch et al., 2004; Chu et al., 2004). Among these constituents, sediments are a serious concern (Ashmore, 1993; Harden, 1993; Conroy et al.,

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2006; Grismer, 2007; Juracek and Ziegler, 2007) because they could shrink the storage volume of reservoirs and lakes, lower the conveyance capacity of streams, damage the recreational and aesthetic values of water bodies, and be a carrier of major pollutants e.g., pesticides and pathogens. In response, researchers and practitioners (e.g., Vaché et al., 2002; Bracmort et al., 2006) have developed best management practices (BMPs) for sediment reductions. In parallel, some other researchers (e.g., Hibbert et al., 1974; Richardson et al., 1979; Hibbert, 1983; Dugas and Mayeux, 1991; Bednarz et al., 2001; Greer, 2005) have examined the feasibility of brush control to increase water availability. Presumed to increase runoff by reducing evapotranspiration, brush control is specific to arid and semi-arid regions (e.g., the north central Texas in the United States of America or U.S.) and fits into the research theme of forest impact on water yield as reviewed by Bosch and Hewlett (1982). For the three brushes of saltcedar (Tamarix ramosissima Ledeb), ashe juniper (Juniperus ashei Buch), and mesquite (Prosopis sp.), which are typical in the arid and semi-arid regions in the United States (U.S.), the results indicated that water can be salvaged by controlling dense stands of saltcedar in riparian zones, dense stands of Ashe juniper in areas with rapid subsurface flow (e.g., where springs are present), and mesquite on soils that develop deep cracks when dry (Wilcox et al., 2005). However, these studies

329

reached no consensus with respect to the amount of water that can be salvaged and the percentage of the salvaged water that can be available for usage. Hibbert et al. (1974) and Hibbert (1983) studied the chaparral conversion potential in Arizona and its effects on water yield improvement. These authors found a positive relationship between the water yield increase and the annual rainfall amount. The water yield increased approximately 1 mm for each 4 mm increase in precipitation over a 400 mm threshold value. Richardson et al. (1979) found that the water yield decreased following the treatment of brushes on Texas rangelands because of the increased surface roughness and depression storage, but the water yield started to increase sometime after the treatment because the depression storage decreased with time. Based on the data collected following the removal of 8700 ha of saltcedar along the 132 km AcmeArtesia reach of the Pecos River in New Mexico, Weeks et al. (1987) found that the annual water use by saltcedar was about 305 mm greater than the use by the replacement vegetation. However, Welder (1988) found no detectable change in streamflow along this study reach as a result of clearing the saltcedar. Owens and Ansley (1997) determined that a mature Ashe juniper can transpire 305–430 mm water per year, which was about 38 mm greater than the annual transpiration rate of herbaceous vegetation (Dugas et al., 1998). Dugas and Mayeux

Fig. 1 – Map showing the location of the Cowhouse Creek watershed, the boundaries of the study area and its 130 subbasins, and the National Weather Service (NWS) station and the U.S. Geological Survey (USGS) flow gauging station, where the data were used in this study.

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(1991) stated that the removal of mesquite in the Rolling Plains of Texas would result in a detectable, but small, increase of water yield. The measurements by Greer (2005) in two comparison plots within the Leon River watershed in Texas indicated that the infiltration rates increased following the removal of Ashe juniper but started to decrease 11 months later. The inconsistent results of these field experiments can be partially attributed to their being conducted at distinctly different spatial scales of small plot, hillslope, and small catchment (i.e., subbasin within a watershed). Although the findings from experiments at these three scales are important, the greatest interest in using brush control to increase water yield is at watershed scale (Wilcox et al., 2005). Nevertheless, because field experiments at watershed scale are technically difficult and financially prohibitive, spatially distributed hydrologic models, such as the Soil and Water Assessment Tool (SWAT), proved to be very useful in evaluating effects of vegetation removal on water yield (e.g., Bednarz et al., 2001; Wu et al., 2001; TAES, 2002; TSSWCB, 2003; Afinowicz et al., 2005) as well as sediment yield. Bednarz et al. (2001) predicted that removing about 80,970 ha of shrubs from the 323,885-ha Pedernales River watershed in Texas would increase the average annual water yield by 127 mm ha−1 treated area. Hereinafter and unless otherwise noted, changes of water and sediment yields were presented as per hectare treated area rather than per unit total drainage area. The treated area was designated the area with range brush removed and/or converted into range grasses for description purposes. Wu et al. (2001) estimated that in the Cusenberry Draw in western Edwards Plateau, the annual runoff would increase by 75 mm as a result of the removal of Ashe juniper. TAES (2002) predicted that the mesquite removal in the Lake Arrowhead watershed in north central Texas would increase the annual water yield by 127 mm. TSSWCB (2003) predicted that the removal of range brush in the Palo Pinto Reservoir watershed would increase the annual water yield by 165 mm. Afinowicz et al. (2005) predicted that the annual water yield in the North Fork of the Upper Guadalupe River watershed would be increased by 38 mm. All of these modeling studies predicted that reducing the amount of range brush would increase water yield, while the predicted increases had a very large variation and were not in agreement with those from field observations (Wilcox et al., 2005). The studies except for Afinowicz et al. (2005) tended to overestimate the water yield increases by a factor of three or greater. These field experiments and modeling studies did not explicitly examine how the interactions between soils and vegetation affected the predicted water yield increases. Given the importance of these interactions in determining the hydrologic condition of a watershed and the partition of water within the hydrologic cycle (Wilcox et al., 2005), this study hypothesized that the prediction accuracy of water yield increase can be improved by taking into account the land use–soil interactive effects and that the predicted yields should be interpreted on an individual soil basis. In addition, the studies cited above did not address effects of brush control on sediment yield, while Greer (2005) reported evident increases of sediment yield as a result of brush removal. Thus, the objective of this study was to examine the land use–soil interactive effects on water and sediment yields.

The study was conducted in the 117,845-ha drainage area upstream of the U.S. Geological Survey (USGS) flow gauging station 08101000 in the Cowhouse Creek watershed located in north central Texas (Fig. 1).

2.

Description of SWAT

SWAT is a physically based, watershed-scale, continuoustime, distributed-parameter hydrologic model that uses spatially distributed data on topography, land use, soil, and weather for hydrologic modeling and operates on a daily time step (Arnold et al., 1998; Arnold and Fohrer, 2005). Based on topography, SWAT subdivides a watershed into a number of subbasins for modeling purposes. Each subbasin delineated within the model is simulated as a homogeneous area in terms of climatic conditions, but additional subdivisions are used within each subbasin to represent different soils and land use types. Each of these individual areas is referred to as a hydrologic response unit (HRU) and is assumed to be spatially uniform in terms of soils, land use, and topography. SWAT is composed of three major components, subbasin, reservoir routing, and channel routing, each of which includes several subcomponents. For example, the subbasin component consists of eight subcomponents: hydrology, weather, sedimentation, soil moisture, crop growth, nutrients, agricultural management, and pesticides. The hydrology subcomponent includes surface runoff, lateral subsurface flow, percolation, ground water flow, snowmelt, evapotranspiration, transmission losses, and ponds. Detailed descriptions of the methods used in modeling these components and subcomponents can be found in Arnold et al. (1998), Srinivasan et al. (1998), and Neitsch et al. (2002a). In this study, the Soil Conservation Service (SCS) runoff curve number method, with the SCS curve number adjusted according to soil moisture conditions (Arnold et al., 1993), was used to estimate surface runoff; the Hargreaves method (Hargreaves and Samani, 1985) was used to estimate potential evapotranspiration (PET); and the Muskingum method (Chow et al., 1988) was used for channel routing. SWAT uses four storage volumes to represent the water balance in each HRU in the watershed: snowpack, root zone (0–2 m), shallow aquifer (2–20 m), and deep aquifer (>20 m). The root zone can be subdivided into multiple layers. For the SCS curve number method, the excess water available after accounting for initial abstractions and surface runoff infiltrates into the soil. A storage routing technique is used to simulate the flow through each soil layer. SWAT directly simulates only the saturated flow and assumes that water is uniformly distributed within a given layer. The unsaturated flow between layers is indirectly modeled using depth distribution functions for plant water uptake and soil water evaporation. Downward flow occurs when the soil water in the layer exceeds field capacity and the layer below is not saturated. The rate of downward flow is governed by the saturated hydraulic conductivity. Lateral flow in the root zone is simulated using a kinematic storage routing technique that is based on slope, slope length, and saturated conductivity. Upward flow from a lower layer to the layer above is regulated by the soil water to field capacity ratios of the two layers. Percolation

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0.15 9.8–10 D Tencee TX629

Gravel

Wilson-Burleson-Mabank TX609

Clay

D

0.23–12

0.43

Permeability increases with the depth Permeability decreases with the depth Permeability is almost constant throughout the soil profile 0.10 0.32 0.37 0.32 280–450 0.09–0.13 8.4–11 1.3–5.7 A D D B Jalmar-Penwell Kaufman-Tinn-Gladewater Kimbrough Reagan-Hodgins-Iraan TX244 TX251 TX260 TX463

Fine sand Clay Loam Clay loam

0.43

USLE erodibility factor (0.013 t m2 h/m3 t cm) Hydraulic conductivity (mm/h)

1.2–120 D Sandy clay, clay Faddin TX176

Fig. 2 – Soils in the study area as classified in the State Soil Geographic (STATSGO) database. The properties of the soils are presented in Table 1.

Hydrologic soil group (HSG)

The 117,845-ha drainage area upstream of station USGS 08101000 within the 188,715-ha Cowhouse Creek watershed in north central Texas (Fig. 1), designated TXCCW for description purposes, was taken as the study area. TXCCW covers portions of five Texas counties: Comanche, Coryell, Hamilton, Lampasas, and Mills. The soils in TXCCW (Fig. 2 and Table 1) are dominated by clay and sandy clay, with an infiltration rate ranging from 0.8 to 332 mm/h (Mace et al., 1999; TCEQ, 2007). The land use within the study area (Fig. 3) consists of almost entirely range brush (55.0%) and range grasses (40.0%), and the rest is composed of forest (4.4%), waters (0.4%), and agriculture (0.1%). TXCCW has a very low topographic relief; its local relief is less than 5 m and global relief is only up to 340 m,

Texture

The study area

Name

3.1.

Soil code

Materials and methods

Table 1 – Properties of soils in the study area as classified in the State Soil Geographic (STATSGO) database.

3.

Remark

from the bottom of the root zone is recharged into the shallow aquifer. SWAT simulates sediment yield using the Modified Universal Soil Loss Equation (MUSLE; Williams, 1995). MUSLE predicts the amount of eroded soils in a HRU that would be delivered into the channel of the inclusive subbasin. In the channel network, sediment transport is modeled as a function of two processes, deposition and degradation, operating simultaneously in the reach (Bagnold, 1977; Williams, 1980). The maximum amount of sediment that can be transported from a reach segment is a function of the peak channel velocity. SWAT provides two options to compute deposition and degradation (Neitsch et al., 2002a). The first option is to use the same channel dimensions for the entire simulation, whereas, the second option is to simulate downcutting and widening of the channel and update channel dimensions throughout the simulation. The first option was used in this study.

Permeability decreases with the depth

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with an annual average value of 2.8 m3 s−1 . The streamflows in spring are predominantly generated from rainfall runoff, but the streamflows in the other three seasons are predominantly fed by the Edwards-Trinity aquifer (USGS, 2005). The data also indicate that the daily peaks range from 0.1 to 996.8 m3 s−1 and that the instantaneous peaks could be up to 3115 m3 s−1 as occurred on 20 December 1991. The peak for a water year (December to November) could occur in any season. In this study, the data on daily streamflows for the water years of 1985 through 2006 were used to calibrate and validate a SWAT model, which has a modeling domain covering the upper 62% of the Cowhouse Creek watershed.

3.2.

Fig. 3 – Major land uses in the study area as classified in the 30-m National Land Cover Data 2001 (NLCD 2001). Range brush accounts for 55% of the study area in size, while range grasses account for 40%.

with the elevation ranging from 150 to 490 m. The Cowhouse Creek has an average sinuosity of 1.3 and is characterized by broad flat alluvial floodplains, river terraces, and lower Cretaceous Walnut Clay with adjacent ridges of the Edwards-Duck Creek-Comanche Peak limestone (Nordt, 2004). The National Weather Service (NWS) National Climate Data Center (NCDC) collects data on daily precipitation and minimum and maximum temperatures at station PR413485, which abuts TXCCW (Fig. 1). An examination of the data revealed that the records for the period from 1 January 1980 to 31 December 2006 had only 32 missing values for precipitation and had no missing value for temperatures. The missing values were generated by the weather generator that is an embedded component of the SWAT software package (Neitsch et al., 2002a). This was expected to exert only a limited influence on the simulation results because of the low percentage (less than 0.32%) of missing data. The available data indicated an annual average precipitation of 865 mm. The annual average daily temperature ranged from −20 ◦ C in winter to 45 ◦ C in summer, with a mean of 19 ◦ C. During the three summer months, the temperature could be 32 ◦ C or higher for 75 days; during the three winter months, 30 days could have a temperature below zero. Overall, TXCCW is a cool, windy, and wet climate (Greer, 2005). These climatic conditions may make the Hargreaves method an appropriate approach for PET estimation (Martinez-Cob and Tejero-Juste, 2004). In addition, the Hargreaves method was used for PET estimation because the additional data e.g., net solar radiation, required by the other two SWAT provided methods, Penman-Monteith (Penman, 1956; Monteith, 1965) and Priestley–Taylor (Priestley and Taylor, 1972), were unavailable. USGS has been monitoring daily streamflows at station USGS 08101000 since 1 October 1950. The data indicate that the Cowhouse Creek is a seasonal stream, with low flows in summer and fall, intermediate flows in winter, and high flows in spring. The daily streamflows vary from 0 to 1000 m3 s−1 ,

Data inputs and model set up

The basic model inputs included the 30 m National Elevation Dataset (NED), the 30 m National Land Cover Data 2001 (NLCD 2001), the State Soil Geographic (STATSGO) database, and the National Hydrography Dataset (NHD). The data on NED, NHD, and NLCD 2001 were obtained from USGS (2008), whereas the STATSGO data were obtained from the U.S. Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) (USDA–NRCS, 2008). These data are stored in the AvSWAT-X required formats (Neitsch et al., 2002b) and were directly taken as the inputs. AvSWAT-X is an upgraded ArcView interface for SWAT (Di Luzio et al., 2002, 2004). NED was developed by merging the highest resolution, best quality elevation data available across the United States into a seamless raster format (USGS, 2001a). NLCD 2001 was developed to distinguish 16 different classes (Homer et al., 2004). The NLCD 2001 data indicate 14 types of land uses in the study area. Fig. 3 shows the spatial pattern of the three major land uses, namely range brush, range grasses, and forest. STATSGO was collected in the USGS 1:250,000 scale, 1◦ by 2◦ topographic quadrangle units and merged and distributed as statewide coverages (USDA-SCS, 1993). Features were edge-matched between states. The STATSGO database has a county-level resolution and is generally used for regional, multistate, river-basin, state, and multicounty resource planning, management, and monitoring. For the study area, the STATSGO data classify the soils into seven types (Fig. 2 and Table 1). NHD is a comprehensive set of digital spatial data that contains information about surface water features such as lakes, ponds, streams, rivers, springs, and wells (USGS, 2001b). This study utilized the NHD stream feature as the reference surface water drainage network to delineate subbasins for modeling purposes. The AvSWAT-X interface was used to delineate the boundaries of the entire study area and its subbasins, along with their drainage channels. The boundaries for the subbasins were determined by trial and error to ensure that the delineated drainage channels closely match the drainage network presented by the NHD data. As a result, TXCCW was subdivided into 130 subbasins, with sizes ranging from 5.4 to 4372.0 ha. For each of the 130 subbasins, the NLCD 2001 and STATSGO data were overlaid to define multiple HRUs for the model. With the SWAT suggested threshold levels of 20% for land use and 10% for soil (Neitsch et al., 2002a; Wang and Melesse, 2005), the AvSWAT interface defined 546 HRUs for the model, i.e., 1–8

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HRUs for a given subbasin. The interface uses the first threshold value to eliminate minor land uses in each subbasin and the second threshold value to eliminate minor soils within a land use area. Subsequently, the interface reapportions the remaining land uses in the subbasin and the remaining soils in the land use area. The details on the elimination and reapportioning processes can be found in Di Luzio et al. (2002) and Neitsch et al. (2002a). In this study, land uses that cover less than 20% of the subbasin area were eliminated. The remaining land uses were reapportioned so that 100% of the subbasin area was modeled. In addition, soils that cover less than 10% of the land use area were eliminated. The remaining soils were reapportioned so that 100% of the land use area was modeled. The data on daily precipitation and minimum and maximum temperatures of the NWS station PR413485 (Fig. 1) were preprocessed into database files with the SWAT required format for a simulation period extending from 1 January 1980 to 31 December 2006. The period from 1 January 1980 to 30 November 1984 was used to allow the initial values of the model parameters to reach equilibrium. The few missing values on precipitation were simulated by the weather generator embedded in SWAT (Neitsch et al., 2002a). The remaining period (1 December 1984 to 30 November 2006) was used for model evaluation and assessment of the land use–soil interactive effects.

3.3.

Model evaluation method

The daily streamflows observed at station USGS 08101000 from 1 December 1984 to 30 November 1996 were used to calibrate the model, which then was validated using the observed daily streamflows at the station from 1 December 1996 to 30 November 2006. The calibration was implemented by manually adjusting model parameters to make the differences as small as possible over the calibration period between: the model generated and observed daily streamflows; the monthly volumes calculated on the basis of the modeled and observed daily streamflows; and the exceedence times for various flow thresholds calculated on the basis of the modeled and observed daily streamflows (Wang and Melesse, 2005, 2006). The model performance was judged using visualization plots and duration curves showing the modeled versus observed streamflows at the outlet of TXCCW, and two commonly used statistics, namely coefficient of determination (R2 ; Johnson and Wichern, 1998) and Nash–Sutcliffe coefficient (E2 ; Nash and Sutcliffe, 1970). R2 measures the proportion of variability in the observed streamflows that is accounted for by the model. The value for R2 can range from 0 to 1, with higher values indicating a better model performance. E2 is computed as

n 2

E =1−

2

i=1

i − Qi ) (Qobs sim

i=1

i −Q ¯ obs )2 (Qobs

n

(1)

i i where Qsim and Qobs are the simulated and observed streamflows, respectively, on the ith time step, and Q¯ obs is the average i across the n evaluation time steps. of Qobs The value of E2 can range from −∞ to 1, with higher values indicating a better overall fit and 1 indicating a perfect fit. A

333

negative E2 indicates that the simulated streamflows are less reliable than using the average of the observed streamflows, while a positive value indicates that they are more reliable than using this average. Based on Motovilov et al. (1999), the simulated streamflows are considered “good” for values of E2 > 0.75, while for values of E2 between 0.75 and 0.36, the simulated streamflows are considered “satisfactory.” This criterion of E2 is consistent with, but has fewer ratings than, the one suggested by Moriasi et al. (2007). Because E2 is dependent on a number of factors, including the evaluation time step e.g., daily versus monthly, the length of evaluation period e.g., 5 versus 10 years, and the data quality, and because E2 usually exhibits large variations and is somewhat subjective (Gassman et al., 2007), fewer ratings may be more feasible and preferred for certain applications. For this reason and because the criteria for evaluation time steps other than monthly have not been well established (Moriasi et al., 2007), this study used the aforementioned criterion suggested by Motovilov et al. (1999) for daily, monthly, seasonal, and annual time steps alike, while it is aware that a stricter performance rating may be warranted as the evaluation time step increases. Because observed data on sediment concentration or loading were unavailable for the study area, the sediment component of the model could not be calibrated and validated. However, the six sediment-related parameters, namely ADJ PKR, PRF, SPCON, SPEXP, CH EROD, CH COV (Table 2), were empirically adjusted to make the predicted sediment yield at USGS 08101000 closely match the value reported by Narasimhan et al. (2007). In the Cedar Creek watershed, Narasimhan et al. (2007) conducted a comprehensive field survey at 56 sites across the watershed and in one lake. Subsequently, using the Rapid Assessment Point Method (RAP-M) developed by Windhorn (2001) and adopted by NRCS, the authors estimated that the average annual total sediment yield for the Cedar Creek watershed would be 1.72 t ha−1 , of which 0.59 t ha−1 could be originated from the stream bank/bed erosion and 1.13 t ha−1 from the overland erosion. Because the Cowhouse Creek watershed is only about 150 km south of the Cedar Creek watershed, this study assumed that the sediment yield for TXCCW would follow this similar magnitude and proportional pattern i.e., 34.3% of the sediment from the stream bank/bed erosion and 65.7% from the overland erosion.

3.4. Assessment method of land use–soil interactive effects The assessment was implemented on an individual soil basis. That is, the range brush covering a soil was assumed to be replaced by range grasses, while the land uses for the other soils were kept unchanged. For a given soil, the replacement was implemented by redefining the HRUs with a land use of range brush to the ones with a land use of range grasses. As a result, the physiological parameters (e.g., maximum leaf area index; Neitsch et al., 2002a,b) for range brush were substituted for those for range grasses. In addition, the overland hydrologic parameters e.g., Manning’s n, for these redefined HRUs were estimated based on the assumed combination of the soil and range grasses. Furthermore, because range grasses have a lower erosion resistance than range brush (Hedrick et al.,

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1966), the USLE (Universal Soil Loss Equation; Williams, 1980, 1995) C-factor for range brush was smaller than that for range grasses. The C-factors for range brush and range grasses were extracted from the SWAT Land Cover/Plant Growth database. For the study area, the combinations of soils and hypothetical land uses formulated seven different options (Table 3). Besides, the option 8 assumed that all range brush covering the study area would be converted to range grasses, whereas, the base condition was defined in accordance with the land uses presented by the NLCD 2001 data. Subsequently, the calibrated SWAT model was used to predict the water and sediment yields for the base condition as well as the eight options. The predicted annual average yields at the outlet of TXCCW for the eight options were compared with the corresponding values for the base condition to examine the influences of land use changes i.e., conversions of range brush to range grasses, as categorized by soil types. To further scrutinize the influences, for each subbasin, the predicted daily sediment yields for the base condition or a given option were arithmetically averaged across the entire evaluation period to calculate the corresponding mean. Also, for each subbasin, the frequency associated with the predicted sediment yields was calculated as the ratio of the number of days with a nonzero sediment contribution to the total number of evaluation days i.e., 8358. The calculated means and frequencies were mapped as low, medium, and high categories. The three categories were defined based on the first and third quartiles calculated using a pooled dataset of the corresponding means. The dataset, generated by pooling together the subbasin means, was used to calculate the first and third quartiles. Rounded to the nearest zero or fifth hundredth, the two quartiles were taken as the broken points of the three categories. For this purpose, the low category was defined as having a value less than the first quartile, the high category, on the other hand, was defined as having a value greater than the third quartile. The medium category was defined as having a

value between these two quartiles. These maps were visually compared to identify whether and how the soils and land use changes would interactively affect the sediment contributions from the areas within TXCCW.

4.

Results and discussion

4.1.

The calibrated model

As a result of the calibration, the SWAT model took the AvSWAT-X estimated values for CN2. However, the other eight parameters, namely SURLAG, MSK CO1, MSK CO2, REVAPMN, GW REVAP, GWQMN, ESCO, and EPCO were adjusted to have values presented in Table 2. Subsequently, the six sediment-related parameters of CH COV, CH EROD, ADJ PKR, PRF, SPCON, and SPEXP were adjusted to have values of 0.022, 0.090 cm h−1 pa−1 , 1.52, 1.0, 0.05, and 1.5, respectively. With these parameter values, the model predicted that the annual average sediment yield at the TXCCW outlet would be 1.76 t ha−1 , of which 0.66 t ha−1 would be originated from the stream bank/bed erosion and 1.10 t ha−1 from the overland erosion. These predicted values were reasonably compatible with the results reported by Narasimhan et al. (2007). The calibrated model had a marginally good or good performance in simulating the monthly and seasonal mean discharges at the outlet of the study area (E2 > 0.63; Table 4). The model did a very good job in reproducing the observed streamflow volume, as indicated by a prediction error of less than 1% (Moriasi et al., 2007). The good model performance was also verified by examining the visualization plots showing the observed versus predicted streamflows (Fig. 4a). The model predicted monthly mean discharges were very close to the corresponding observed values (Fig. 4b). However, the model tended to overestimate the annual average discharges for some water years, while for the other years, the discharges were underpredicted (Fig. 4a). In

Table 2 – List of adjusted parameters. Parameter SURLAG (day) MSK CO1 MSK CO2 REVAPMN (mm H2 O)

Definition

ESCO EPCO CN2

Surface runoff lag coefficient Muskingum translation coefficient for normal flow Muskingum translation coefficient for low flow Threshold depth of water in the shallow aquifer for “revap” or percolation to the deep aquifer to occur Groundwater “revap” coefficient Threshold depth of water in the shallow aquifer required for return flow to occur Soil evaporation compensation factor Plant water uptake compensation factor SCS curve number for soil moisture condition II

CH COV CH EROD (cm/(h pa)) ADJ PKR PRF SPCON SPEXP

Channel cover factor Channel erodibility factor Peak rate adjustment factor for sediment routing in subbasin Peak rate adjustment factor for sediment routing in reach Coefficient in sediment transport equation Exponent in sediment transport equation

GW REVAP GWQMN (mm H2 O)

a b

Rangea

Calibrated value

1.0–12.0 0.0–10.0 0.0–10.0 0.0–500.0

2.0 2.5 2.5 1.0

0.0–0.2 0.0–5000.0

0.15 500

0.01–1.0 0.01–1.0 60.0–95.0 0.0–1.0 0.0–1.0 1.0–3.0 1.0–3.0 0.0001–0.01 1.0–2.0

0.27 0.85 Defaultb 0.022 0.090 1.52 1.00 0.05 1.50

The ranges for the parameters were based on Neitsch et al. (2002a,b) and Wang and Melesse (2005, 2006). The values for this parameter was derived by the AVSWAT-X for SWAT 2005 interface (Di Luzio et al., 2004) and varied from one hydrologic response unit (HRU) to another.

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Table 3 – The simulated optionsa . Soil code

TX176 TX244 TX251 TX260 TX463 TX609 TX629 a

Rang brush

Base condition

Area (ha)

% of the soil area

1361.2 2653.7 9676.3 148.0 5447.4 16471.2 14105.5

18.2 53.1 33.0 19.7 41.6 45.7 58.9

NC NC NC NC NC NC NC

Option 1

2

3

4

5

6

7

8

B→G NC NC NC NC NC NC

NC B→G NC NC NC NC NC

NC NC B→G NC NC NC NC

NC NC NC B→G NC NC NC

NC NC NC NC B→G NC NC

NC NC NC NC NC B→G NC

NC NC NC NC NC NC B→G

B→G B→G B→G B→G B→G B→G B→G

NC stands for “no change” of the land uses, whereas, B → G signifies that the range brush covering the soil was assumed to be converted to range grasses.

Fig. 4 – Plots showing the observed versus model predicted (a) annual average and (b) monthly mean discharges at the outlet of the study area.

Fig. 5 – Duration curves of the observed and model predicted daily streamflows at the outlet of the study area for the (a) calibration and (b) validation periods.

Table 4 – Statistics of daily, monthly, and seasonal discharges at the outlet of the study areaa . Calibrationb

Statistics Obs. (m3 /s) Daily Monthly Seasonal a

b c

3.02 3.02

Pred. (m3 /s) 3.04 3.04 3.04

Validationc R2

E2

Obs. (m3 /s)

Pred. (m3 /s)

R2

E2

0.05 0.64 0.79

−0.11 0.63 0.76

3.65 3.65 3.65

3.96 3.96 3.96

0.03 0.22 0.04

−0.47 −0.07 −0.37

Obs. is the observed value, Pred. is the predicted value, R2 is the coefficient of determination, and E2 is the Nash–Sutcliffe coefficient defined by Eq. (1). The calibration period is from 1 December 1984 to 30 November 1996. The validation period is from 1 December 1996 to 30 November 2006.

336

d

c

b

The predicted sediment loadings for the option 8 were lower than those for the option 7 because the conversion of all range brush into range grasses would retard sediment from the uplands being transported downstream to the outlet of the study area. The calibration period is from 1 December 1984 to 30 November 1996. The validation period is from 1 December 1996 to 30 November 2006. The evaluation period includes both the calibration and validation periods. a

3.84 267,946 3.82 273,116 3.52 263,714 3.46 255,152 3.46 265,003 3.53 328,449 3.46 265,730 Evaluationd Streamflow (m3 /s) Sed. loading (t/year)

3.46 213,832

3.46 325,656

4.37 343,163 4.34 346,947 4.07 338,113 3.96 325,342 3.96 338,229 4.07 340,971 3.96 339,136 Validationc Streamflow (m3 /s) Sed. loading (t/year)

3.96 226,108

3.96 337,850

3.38 205,265 3.38 211,590 3.06 201,715 3.04 196,660 3.04 203,981 3.08 203,230 3.04 203,715 3.05 204,557 3.04 203,602 Calibrationb Streamflow (m3 /s) Sed. loading (t/year)

5 1

2

3

4

Option Base condition Period

Table 5 – The model predicted annual average total streamflow and sediment loading at the outlet of the study area.

6

7

8a

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particular, the model noticeably underestimated the streamflow occurred in 1991, when a record-breaking peak discharge was recorded at USGS 08101000 on December 20 as a result of an extreme storm (USGS, 1998). The storm had daily rainfall totals exceeding 102 mm at numerous locations across the study area. In addition, the model underestimated the daily streamflows higher than 100 m3 s−1 and the streamflows lower than 50 m3 s−1 , but the model overestimated the flows between these two values (Fig. 5a). The simulated daily streamflows were less reliable than using the average of the observed values (E2 < 0). Again, this might be due to the SWAT’s weakness in simulating short-term thunderstorms as well as very dry conditions (Feyereisen et al., 2007). For this reason, the prediction of sediment was evaluated at monthly and annual time steps only. The underestimation of the 1991 storm event was likely due to SWAT being weak in simulating large extreme storm events (Wang and Melesse, 2006) such as the one occurred in 1991. During a large extreme storm event, the soil moisture condition varies greatly within a time period (Brutsaert, 2006) shorter than the SWAT simulation time step of 1 day. However, SWAT uses the daily average soil moisture condition, which is usually drier than that at the peak time, to estimate the peak discharge, and thus is likely to give an underpredicted value (Wang et al., 2008). Also, the SCS curve number method used by SWAT is not parameterized to reflect influences of rainfall intensity, which could control the runoff generation process for a large extreme storm event (Wang et al., 2008). During the validation period, the model was unable to successfully simulate another two extreme large storms that occurred in 1997 and 2004 (Fig. 4), resulting in negative E2 values (Table 4). Again, this can be attributed to that SWAT may be weak in simulating large extreme storm events. As with for the calibration period, the model underestimated the daily streamflows higher than 100 m3 s−1 and the streamflows lower than 50 m3 s−1 , but the model overestimated the flows between these two values (Fig. 5b). However, the model did a very good job in reproducing the volume of the observed streamflows, as indicated by a low prediction error of less than 8% (Moriasi et al., 2007). Overall, the model had a performance comparable with, or even better than, the performances of SWAT models reported in literature (e.g., Vazquez-Amábile and Engel, 2005; Du et al., 2006; Van Liew et al., 2007). Thus, this calibrated SWAT model was judged to have simulation accuracy sufficient for evaluating the land use–soil interactive effects on sediment contributions for the study area of TXCCW.

4.2. The effects on water yield and sediment loading at the outlet The conversion of range brush to range grasses would have no detectable effects on the increase of water yield unless the conversion area is large. This is consistent with the findings of the 94 field experiments reviewed by Bosch and Hewlett (1982). The options 1, 2, 4, and 5, which were assumed to have conversion areas ranging from 148.0 to 5447.4 ha (Tables 1 and 3), were predicted not to affect the water yield (Table 5). For the study area, a threshold value for conversion area above which

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337

Fig. 6 – Maps showing the predicted (a) sediment yield and (b) percent chance to have nonzero daily sediment yields from the subbasins of the study area for the base condition defined in Table 3.

the effects would become detectable was likely to be between 5500 and 10,000 ha. Theoretically, the threshold value may be determined as the conversion area at which the decreased evapotranspiration as a result of the removal of range brush is just larger than the increased evapotranspiration as a result of the growth of range grasses in this converted area. That Welder (1988) found no detectable change in streamflow might be because the 8700-ha saltcedar removal was below the required threshold value. On the other hand, the removal area of mesquite in the study conducted by Dugas and Mayeux

(1991) might be just above the threshold value, as indicated by a detectable, but small, increase of water yield. The options 3, 6, 7, and 8 were predicted to increase the water yield, as the predicted total streamflows at the outlet are greater than that for the base condition (Table 5). The predicted water yield increases for the validation period were greater than the corresponding increases for the calibration period. This was because the validation period had a wetter hydrologic condition than the calibration period (3.65 m3 s−1 versus 3.02 m3 s−1 ; Table 4), leading to a stronger relationship

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Fig. 7 – Maps showing the predicted (a) sediment yield and (b) percent chance to have nonzero daily sediment yields from the subbasins of the study area for the option 3 defined in Table 3.

between brush removal and increased water yield (Wilcox et al., 2005). For the option 8, the annual water yield was predicted to increase by 24 mm from the 49,863 ha treated area i.e., the area where range brush was assumed to be replaced by range grasses. This predicted increase was just above half of the value reported by Afinowicz et al. (2005) and was judged to be realistic. The increase can be attributed to the reduced interception and transpiration rates from the removal of range brush (TSSWCB, 2003). For the other three options, the predicted increases of annual water yield were between 11 and

80 mm from the corresponding treated areas. The results indicated that the conversion on the TX629 soil would be more beneficial than the conversions on the TX251 and TX609 soils. The TX629 soil has a moderately high permeability of about 10 mm h−1 (Table 1) and is located in the upland area (Fig. 2), thus water can move rapidly through the soil, leading to a strongest linkage between the removal of range brush and increased water yield (Wilcox et al., 2005). In addition, although the TX609 soil has a greater permeability than the TX251 soil, the removal of range brush on the

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Fig. 8 – Maps showing the predicted (a) sediment yield and (b) percent chance to have nonzero daily sediment yields from the subbasins of the study area for the option 6 defined in Table 3.

TX251 soil was predicted to have a larger increase of annual water yield than that for the TX609 soil (23 mm versus 11 mm from the corresponding treated areas). One explanation may be that the TX251 soil is adjacent to the Cowhouse Creek and its major tributaries (Fig. 2), where range brush may be using shallow groundwater that is hydrologically connected to streamflow (Wilcox et al., 2005). Another explanation may be that the water to be salvaged by the removal of range brush on the TX251 soil could flow into the streams without

being subject to additional evapotranspiration and infiltration losses. Further, soils in different hydrologic soil groups (HSGs; USDA–NRCS, 2004) have different runoff potentials. HSG D soils have an overall higher runoff potential than B soils, which in turn have a higher runoff potential than A soils. As a result, the conversion of range brush on a D soil would be more efficient in increasing water yield. Therefore, the discrepancies of the predicted water yield increases in the aforementioned studies (e.g., Bednarz et al., 2001; Wu et al., 2001; TAES, 2002;

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Fig. 9 – Maps showing the predicted (a) sediment yield and (b) percent chance to have nonzero daily sediment yields from the subbasins of the study area for the option 7 defined in Table 3.

TSSWCB, 2003; Afinowicz et al., 2005) might be explainable if the predicted values could be interpreted on an individual soil basis. In an adverse effect, the conversion of range brush to range grasses increases the sediment loading at the outlet of the study area (Table 5). Although the options 1, 2, 4, and 5 have no detectable effects on water yield, these options are predicted to increase the annual sediment loading by 7.7–346.0 t ha−1 treated area. This may be because range grasses have a lower erosion resistance than range brush (Hedrick et al., 1966). The option 8 was predicted to increase the sediment loading by

around 1.0 t year−1 ha−1 treated area, while the options 3, 6, and 7 were predicted to increase the loading by 4.2–11.9 t ha−1 treated area. The predicted increases for these four options was because the increased overland runoff and streamflows would have more energy for soil erosion and sediment transport (Duan, 2004), and because range grasses have a lower erosion resistance than range brush as indicated by the USLE C-factor. As with water yield, soils have direct influences on the predicted increases of sediment loading. For a soil with a higher USLE erodibility factor and a higher runoff potential, the

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341

Fig. 10 – Maps showing the predicted (a) sediment yield and (b) percent chance to have nonzero daily sediment yields from the subbasins of the study area for the option 8 defined in Table 3.

removal of range brush tended to result in a greater increase of sediment loading. For example, while the annual water yield increase from the removal of range brush on the TX629 soil was predicted to be almost seven times larger than the water yield increase from the removal of range brush on the TX609 soil, the predicted sediment loading increases were very close: 4.2 t ha−1 treated area for the TX629 soil and 3.0 t ha−1 treated area for the TX609 soil. This result was because the USLE erodibility factor of the TX629 soil was only 37% of that of the TX609

soil (Table 1). The predicted large increase of sediment loading as a result of the range brush removal on the TX251 soil can be attributed to this soil being adjacent to the Cowhouse Creek and its tributaries (Fig. 2), which could facilitate the transport of eroded soils into the streams. The removal of range brush could elevate the sediment concentration by 300–1000 mg/L. For the study area, the removal of range brush on the TX629 soil was predicted to be most beneficial to increasing the total water yield with an acceptable increase of sediment loading.

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4.3. The effects on sediment contributions within the study area For the base condition, the model predicted that more subbasins in the upper portion of the study area have medium (5–10 t ha−1 ) and/or high (>10 t ha−1 ) sediment yields, while more subbasins in the lower portion have low (<5 t ha−1 ) and/or medium sediment yields (Fig. 6a). One explanation for this spatial pattern is that the values of the USLE-length-slope factor (Moore and Burch, 1986) for the upper subbasins are greater than those for the lower subbasins. Another explanation is that more forests scatter across the lower portion (Fig. 3), preventing the eroded soils from being transported into the streams. The predicted spatial pattern of sediment yield was not necessarily consistent with the spatial pattern of sediment frequency, defined as the percent chance to have a nonzero daily sediment yield (Fig. 6a versus b). A subbasin that was predicted to have a low sediment yield might be predicted to most frequently (>15%) contribute sediment, whereas, a subbasin that was predicted to have a high sediment yield might be predicted to least frequently (<10%) contribute sediment, and vice versa. The subbasins that were predicted to have a medium sediment yield could contribute sediment either at a low or medium (10–15%) or high frequency. The options 1, 2, 4, and 5 were predicted to barely change the spatial patterns of sediment yield and frequency of the base condition. Because of this similarity, the predicted spatial patterns for these four options are not shown. The option 1 was predicted to change the sediment yield for one upper subbasin from the medium to high category and for one lower subbasin from the low to medium category. In addition, this option was predicted to change the frequency for one upper subbasin from the high to medium category and for another upper subbasin from the low to medium category. Also, this option would change the frequency for one lower subbasin from the medium to high category and for another lower subbasin from the high to medium category. The options 2, 4, and 5 were predicted to change the categories of sediment yield and frequency for one subbasin or two only. One possible explanation was that the TX176 soil is distributed along the Cowhouse Creek main stem, while the TX244 and TX260 soils are isolated in the upper and lower areas with small sizes (Fig. 2). In addition, the TX463 soil might be least sensitive to the removal of range brush for sediment generation and transport, as indicated by a smallest increase of annual sediment loading (7.7 t year−1 ha−1 treated area; Table 5). In contrast, the options 3, 6, 7, and 8 were predicted to change the spatial patterns of sediment yield and frequency of the base condition. The predicted spatial patterns for these four options are shown in Figs. 7–10, respectively. As expected, the predicted changes for a given option would be mainly in the subbasins that include the soil that formulates this option. For example, the option 3 was predicted to change the sediment yield and frequency categories for some of the subbasins adjacent to the Cowhouse Creek main stem (Fig. 7a and b) because the TX176 soil is distributed along the creek (Fig. 2). The predicted spatial patterns for the option 8 (Fig. 10a and b) reflected the interactive effects of all seven soils (Table 1) and the removal of all 49,863 ha range brush on sediment contri-

butions from the geographic locations across the study area. Compared with the other three options, the option 7 would change the spatial patterns at a minimal extent (Fig. 9 versus Fig. 6). Thus, as stated above, the removal of range brush on the TX629 soil would be most beneficial (Figs. 8–10).

5.

Summary and conclusions

This paper has examined the land use–soil interactive effects on water and sediment yields for the 117,845-ha drainage area upstream of USGS 08101000 within the Cowhouse Creek watershed located in north central Texas. For this purpose, a SWAT model was calibrated and validated using the observed daily streamflows at this gauging station from 1 December 1984 to 30 November 2006. Subsequently, the six sedimentrelated parameters were empirically adjusted to make the model predicted total sediment yield at the outlet and its composites i.e., stream bank/bed erosion and overland erosion, closely match the corresponding values reported by Narasimhan et al. (2007). The model was judged to have simulation accuracy comparable with, or even better than that of SWAT models reported in literature. Subsequently, the calibrated model was used to predict the changes of water and sediment yields as a result of the conversion of range brush to range grasses on an individual soil basis. The results indicated that the removal of all 49,863ha range brush would increase the annual water yield by 24 mm ha−1 treated area. The removal of range brush on the TX176, TX244, TX260, or TX463 soil was predicted to have no detectable effects on water yield, whereas, the removal of range brush on the TX609, TX251, and TX629 soils would increase the annual water yield by 11, 23, and 80 mm, respectively. The large increase for the TX629 soil is because it is a HSG D soil with a moderately high permeability and located in the upland area. For a given soil, the increase of water yield was smaller for the calibration period than for the validation period, when the weather was wetter. In addition, the predicted water yield increase for the TX251 soil was larger than that for the TX609 soil, indicating that the linkage between brush removal and increased water yield is stronger in areas adjacent to the stream channels. Conversely, the removal of range brush increases sediment loading regardless of there being no detectable effects on water yield. The removal of range brush on the TX629 soil would be most beneficial because it would result in the largest increase of annual water yield (80 mm ha−1 treated area) but in a small increase of annual sediment loading (4.2 t ha−1 treated area) and a minimal alteration to the existing spatial patterns of sediment contributions within the study area. A reasonable generalization of this study was that the land use–soil interactive effects may need to be considered to develop BMPs e.g., brush control programs, for improving watershed health and sustainability. As with those of the field experiments (e.g., Bosch and Hewlett, 1982; TAES, 2002) and the previous modeling studies in other watersheds (e.g., TSSWCB, 2003), the results of this study are most useful in indicating the direction and relative magnitude of changes of water availability and sediment yield as a function of brush control options on a soil-by-soil basis.

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The absolute magnitudes should be interpreted with a caution because the model was calibrated only against the streamflows at the outlet of the study area and hence uncertainties were expected when this calibrated model was used to reproduce the runoff and sedimentation processes of the subbasins within the study area.

Acknowledgements This study was partially supported by the Tarleton State University Organized Research Grant (ORG) under contract 153118. Also, Mr. T. Shane Gabor, a Research Biologist in Ducks Unlimited Canada, provided partial financial support for dissemination of the research results.

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