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Applicability and Sensitivity of Field Hydrology Modeling by the Soil Plant Air Water (SPAW) Model Under Changes in Soil Properties
Ajoy Kumar Saha1,*, John McMaine1,**
Published in Journal of the ASABE 66(4): 809-823 (doi: 10.13031/ja.15306). Copyright 2023 American Society of Agricultural and Biological Engineers.
1Department of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, South Dakota, USA.
The authors have paid for open access for this article. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License https://creative commons.org/licenses/by-nc-nd/4.0/
Submitted for review on 15 August 2022 as manuscript number NRES15306; approved for publication as a Research Article by Associate Editor Dr. Zhiming Qi and Community Editor Dr. Kati Migliaccio of the Natural Resources & Environmental Systems Community of ASABE on 24 March 2023.
Highlights
Abstract. Agricultural practices can change soil properties and the amount of runoff generated from a landscape. Modeling results could be significantly different than expected if the web soil survey or other commonly used remote sensing applications are used as model inputs without site verification. This study assessed the applicability and sensitivity of the Soil-Plant-Air-Water (SPAW) Model for simulating the runoff (%) and infiltration (%) components of the water balance for various soil physical properties, cover crop, and weather variables. Soil profiles in 135 combinations were developed with three soil classes (sandy loam, silt loam, and clay), five organic matter levels (1%, 2%, 3%, 4%, and 5%), three levels of compaction (low, medium, and high), and three topsoil layer thicknesses (7.6 cm, 11.4 cm, and 15 cm). Also, three cover crop treatments were simulated by modifying surface cover and evapotranspiration during the non-growing season. Finally, two precipitation regimes were considered (Iowa City, IA, as high precipitation and Brookings, SD, as low precipitation) to simulate runoff and infiltration. In total, 810 scenarios were run, resulting in over 300 million data points. This study confirmed that soil texture, bulk density, and topsoil thickness significantly (p<0.01) influence runoff generation and infiltration percentage based on the water balance criterion. Interestingly, the SPAW model had no significant response on runoff (%) and infiltration (%) to organic matter levels changing from 1% to 5%. This simulation demonstrates that runoff estimations can be significantly influenced by soil properties that can change due to agricultural conservation practices (ACPs) or, conversely, by compaction events. Inputs to models must account for these changes rather than relying only on historical or remote sensing inputs.
Agricultural management practices impact the function of soil's physical, chemical, and biological properties(Connolly, 1998; Franzluebbers et al., 2021; Jangid et al., 2008; Sapkota et al., 2012). No tillage or reduced tillage, crop diversification, and cover crop management are under the agricultural conservation practices (ACPs) umbrella (sharing similarities with soil health principles or regenerative agriculture practices) and are implemented to enhance soil ecosystems (Lal, 2013), increase organic matter (Doran, 2002), reduce bulk density (Sapkota et al., 2012), and build soil aggregate and soil structure (Pagliai et al., 2004). With these changes, the infiltration rate increased and the water holding capacity improved, which reduced runoff. However, this magnitude of change in the water balance depends on soil properties (Bormann et al., 2007) and weather (Bronstert et al., 2002). It is important to determine the extent of the change in water balance parameters due to the change in agricultural practices for commonly used water balance estimation techniques. Researchers observed that several years is required to change soil properties and productivity through continuous ACPs, depending on the weather, soil type, land management, and cropping pattern. Much research is conducted to evaluate ACPs by targeting specific soil parameters or specific soil functions with various lengths of practicing, considering various soil depths, different combinations of soil health adoptions, land management, cropping pattern, and different geological settings. The majority of research focused on the impact of soil health or ACPs on soil quality and soil hydraulic function for a relatively short-term (3-10 years) (Aziz et al., 2013; Ghimire et al., 2019; Graham et al., 2021; Ozlu et al., 2019), but there is some research conducted to identify the effects of long-term (>25 years) soil health practices (Fuentes et al., 2004; Tarkalson et al., 2006).
Agro-hydrological models are useful tools for assessing the interactions between soil physical condition, plant growth, management practice, and climate conditions (Connolly, 1998). The Soil-Plant-Air-Water (SPAW) computer-based model is widely used for modeling agricultural field runoff and the hydrological model’s application (Andersen et al., 2010; Saxton et al., 2006). It was developed to simulate the daily hydrological characteristics (e.g., runoff, infiltration, evapotranspiration, irrigation, soil moisture, etc.) of agricultural fields based on crop type, soil properties, management practices, and weather conditions (Saxton et al., 2006; Saxton and Willey, 2006). The model available on the Natural Resource Conservation Service (NRCS) website is relatively complex and provides detailed information about recommended Agricultural Conservation Practices (ACPs) for runoff at daily time steps (Moffitt et al., 2003). It has a long history of being used by different groups of users to simulate soil water balance and crop water stress (Omer et al., 1988; Saxton et al., 1992; Sudar et al., 1981); to evaluate the wastewater storage system (Moffitt et al., 2003; Moffitt and Wilson, 2004); to predict the runoff volume and evaluate the runoff controlling capacity of vegetative treatment areas in Iowa (Andersen et al., 2010); to assess soil moisture characteristics (Aslam et al., 2021; Ben-Asher et al., 2021; Marhaento et al., 2017; Ouyang et al., 2018); to assess the effects chemical inputs like fertilizer and dispositions in agricultural systems (Rosov et al., 2020; Saxton and Willey, 2006); to design constructed wetlands for the detention of agricultural runoff (Millhollon et al., 2009); to estimate soil physical properties (Katwal et al., 2021); to size ponds and lagoons for water impoundment (Classen and Lal, 2012). It is also used for permitting concentrated animal feeding operations (CAFO) at the federal level and for many state general permits (Rosov et al., 2020; US EPA, 2012).
The hydro-geological properties of topsoil layers are essential input parameters in hydrological modeling and play a vital role in simulation results (Saha et al., 2022). Established soil health practices or long-term conservation agriculture can alter the hydrology of agricultural fields through changes to bulk density, organic matter, soil cover, topsoil thickness, root depth, and live root season (Baumhardt et al., 2017). Changes to soil physical properties can alter the field water balance by changing the field evapotranspiration, infiltration rate, and water holding capacity (Kumar et al., 2022; Xu and Mermoud, 2003; Young et al., 2009), which alters the amount of runoff generated. In addition, the magnitude of runoff depends on field soil properties and regional climate characteristics. While protocol and procedures vary, computer-based analysis and soil hydraulic property inputs to SPAW are often determined from the soil survey map (Andersen et al., 2010), previous study information (Omer et al., 1988), or the Web Soil Survey (Soil Survey Staff, 2014). If assumed variables or variables (such as soil texture, organic matter, compaction level, etc.) determined from remote sensing are different than field-measured variables, model applications and runoff determination could result in significant error.
Currently, the National Pollution and Discharge Elimination System (NPDES) and the South Dakota Department of Agriculture and Natural Resources (SDDANR) recommend the SPAW model be used to estimate runoff from concentrated animal feeding operations (CAFOs) feedlots as part of the state and NPDES permitting process (South Dakota DENR, 2020). It is understood that multiple agricultural management practices can affect runoff negatively or positively, but it is unknown how sensitive SPAW is to potential changes in soil properties due to ACPs, especially under different soil textures and climate systems. This analysis will help address whether water balance estimations are valid with a remote site assessment. Because of the importance of SPAW at the federal policy level and within multiple states to determine permits for CAFOs, questions of applicability and sensitivity must be answered to ensure accurate results. The two specific objectives of this study were: (1) to assess the applicability and sensitivity of SPAW modeled field runoff (%) and infiltration (%) to soil physical parameters that may be influenced by ACPs, (2) to assess the applicability and sensitivity of SPAW modeled field runoff (%) and infiltration (%) to soil texture and climate variation.
Materials and Methods
The field module of SPAW is a one-dimensional model that simulates agricultural field water balance from the plant canopy to the soil profile and extends up to rooting depth (Saxton, 2018; Saxton and Willey, 2006). It covers hydrologic processes from the soil surface to the plant canopy and within the subsurface soil profile but does not include any lateral soil water flow (see fig. 1). The USDA/SCS runoff curve number (SCS-CN) method was used in the SPAW model to simulate daily runoff incorporating soil type, antecedent soil moisture, vegetation, and surface conditions (Saxton, 2018; USDA-NRCS, 1997). No stream routing is provided, and observed runoff can be substituted for estimated values. A daily infiltration amount is calculated based on the difference between rainfall and runoff and stored in the uppermost soil layers as currently available capacity permits. Infiltrated water is redistributed between the assigned soil layers by a Darcy tension-conductivity procedure having both downward and upward flow components governed by the soil profiles' physical, chemical, and hydraulic characteristics (such as soil layer textures, organic matter, gravel, density, salinity, water holding capacity, and retention capacity). The SPAW model uses the daily pan evaporation method (default) to measure potential atmospheric evaporation (PET). Then the actual evapotranspiration (AET) is calculated by combining the major AET components: interception evaporation, direct soil surface evaporation, and plant transpiration. Interception is the portion of precipitation intercepted by the earth surface and evaporated afterward. In water balance, percolation is considered the daily water leaving (downward) the bottom layer of the soil profile, and it is temporarily stored in the boundary layer (called the "image" layer in SPAW) just below the profile. The percolated water is upward retrievable and considered negative percolation for groundwater contributions or dry profiles over wet subsurface soil. Deep drainage or deep percolation is observed when the "image" layer is near saturation and additional percolation occurs. In this case, the water releases the "image" layer and contributes to groundwater or interflow. Finally, the water balance was estimated using the following two equations:
P + I = R + f + Ia + e (1)
f = E + T + Ppercolation + D + ?S + e (2)
where
P = precipitation (mm)
I = Net Irrigation (mm)
R = Runoff (mm)
f = Infiltration (mm)
E = Evaporation (mm)
T = Transpiration (mm)
Ia = Interception (mm)
Ppercolation = Percolation (mm)
D = Deep drain/percolation (mm)
?S = Soil moisture change (mm)
e = the computational error during simulation.
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| Figure 1. Hydrological processes in the agricultural field within the SPAW model (Saxton and Willey, 2006). |
Model Setup
Hypothetical SPAW-field models were constructed for this study based on the latest available crop and soil files
(in version 6.02.75). Models were executed to simulate daily runoff for the 'continuous corn' field, having different combinations of the soil profile (by changing soil class, soil compaction level, varying organic matter level, and changing top layer thickness). In every simulation, the agricultural field's water balance was estimated for rainfed agriculture, and no additional fertilizer was applied as a management practice.
Additionally, cover crop practices were included in the modeling process. The model was run for 38 years of climatological parameters for Brookings, South Dakota (SD), and 42 years for Iowa City, Iowa (IA), considering the different weather regimes (based on their long-term low and high precipitation patterns, respectively). The details of the modeling process, including model properties, are given below:
Crop
Our modeling process used the default corn-belt corn crop file available in SPAW to simulate agricultural field hydrology. Crop files' characteristics were adopted from local knowledge of corn belt (Wild, 1988) and guidelines for the average growth description of the corn (Saxton and Willey, 2006). Figure 2 illustrates the geographical location of the corn belt in the United States and the corn plant's growth, yield, and nutrient uptake characteristics curves for this region over a calendar year. In the north-central area, corn is planted/sowed in late April to mid-May and matured and harvested in September. Good hydrological conditions were considered in the modeling process for weather regimes with crop field attributes (or land-use patterns) contoured and row crops.
Soil
Several researchers observed that ACPs such as minimum or zero tillage, crop rotation, mulching (by crop residue), and cover crop, increase organic matter by 71%-100% (Blevins et al., 1983; Rhoton, 2000), decreased bulk density by 3%-9% (Blevins et al., 1983; Sapkota et al., 2012), and increase soil aggregate stability by 19% (Rhoton, 2000), which improves the soil structure, especially within 0-15 cm of the top surface (Pagliai et al., 2004). Therefore, the properties of the top layer of soil have been modified in this modeling study to mimic the possible changes in soil conditions (by changing soil parameters) in the field due to the adoption of different ACPs. There is an in-built soil file (with soil extension) for the soil classes such as clay, sandy loam, and silty loam in SPAW. The soil profile for each soil class is generally covered from the ground surface to 244 cm depth, having eight distinct soil layers with specific soil characteristics (i.e., layer thickness, % sand, % clay, % organic matter, and bulk density) in each layer. The rest of the soil layers are kept unchanged in the default soil files. Therefore, artificially, soil profiles in 135 combinations were developed in SPAW with three soil classes (sandy loam, silt loam, and clay) by varying topsoil layer thicknesses (three layers: 7.6 cm, 11.4 cm, and 15 cm), % organic matter levels (five levels: 1%, 2%, 3%, 4%, and 5%), and compaction (three levels: low, medium, and high) (table 1). Sandy loam and silt loam are common soil types in agricultural systems, while clay is uncommon for agricultural systems and represents the worst-case scenario for runoff.
Climate
This modeling process considered two precipitation regimes, high and low, to generate the hydrological output for different combinations of soil properties in the agricultural field. It assessed the impact of climate scenarios, which strongly dominate runoff generation and consequently in water balancing in the different agro-hydrological systems. Iowa City, IA, precipitation (868 mm) was 48% higher than Brookings, SD, precipitation (588 mm) (fig. 3a). Figures 3b and 3c show the monthly variation of precipitation, temperature, and potential evapotranspiration. The crop growing period (April-September) is quite warm (average maximum and minimum temperatures were observed at 25.8 ? and 13.4 ? for Iowa City, IA, and 22.3 ? and 8.9 ? for Brookings, SD) and wet (651 mm and 470 mm rainfall, respectively, for Iowa City, IA, and Brookings, SD). The long-term rainfall pattern in the growing season is about 66.3% and 86% of potential evapotranspiration, respectively, for Brookings, SD, and Iowa City, IA, which is a favorable climate condition for the rainfed corn production in corn-belt corn.
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| (a) |
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| (b) |
| Figure 2. (a) Region of the Corn-belt in the United States (USA) based on the temporal average of the modified areal Fraction of corn (Fc) values calculated for the years 2010 through 2016 (figure directly from Green et al., 2018), (b) Plant (corn) canopy, greenness, and rooting depth distribution curves over a calendar year shown by three lines. A fourth curve, Yield Sus. (yield susceptibility) represents the relative impact of annual crop water stress on grain yield (Saxton and Willey, 2006). |
Agricultural Management
The model was run in a rainfed condition. Rainfed corn cultivation is common in these weather regimes as the precipitation exceeds 40% of potential evapotranspiration (Robinson and Nielsen, 2015). No additional fertilizer was applied to simplify the modeling process during the simulation period, as fertilizer application is only used in SPAW for nitrogen balancing and grain yield (Saxton et al., 2006). In SPAW, field hydrology is driven by other crop input variables such as canopy, greenness, and rooting depth crop height which are taken from the corn belt crop growth description (Wild, 1988). This crop data is not changed over time by management options such as irrigation and nitrogen fertilizer application. ACPs such as long-term reduced tillage, crop rotation, and cover crop, will change soil properties. Changes to soil properties by ACPs (other than cover crop) were simulated by changing relevant model inputs such as organic matter, topsoil depth, and bulk density (compaction), as discussed in the “Soil” section of the Materials and Methods. Cover crops were defined separately as management scenarios. Three cover crop (CC) treatments (No CC, warm-season CC, and cool-season CC) were developed and included by modifying canopy cover and root depth in the crop characteristics file (Corn-Corn Belt.crop in SPAW) for the non-growing period of the year (October to March). Cover crop characteristics were determined based
| Table 1. List of the artificial 45 topsoil profiles developed in each soil textural class by varying the soil properties of the topsoil layer in the SPAW model. | |||
| Organic Matter (%) | Compaction Levels | Top Layer Thickness (cm) | Topsoil Profile |
| 1% | Low | 7.6 | 1 |
| 11.4 | 2 | ||
| 15 | 3 | ||
| Medium | 7.6 | 4 | |
| 11.4 | 5 | ||
| 15 | 6 | ||
| High | 7.6 | 7 | |
| 11.4 | 8 | ||
| 15 | 9 | ||
| 2% | Low | 7.6 | 10 |
| 11.4 | 11 | ||
| 15 | 12 | ||
| Medium | 7.6 | 13 | |
| 11.4 | 14 | ||
| 15 | 15 | ||
| High | 7.6 | 16 | |
| 11.4 | 17 | ||
| 15 | 18 | ||
| 3% | Low | 7.6 | 19 |
| 11.4 | 20 | ||
| 15 | 21 | ||
| Medium | 7.6 | 22 | |
| 11.4 | 23 | ||
| 15 | 24 | ||
| High | 7.6 | 25 | |
| 11.4 | 26 | ||
| 15 | 27 | ||
| 4% | Low | 7.6 | 28 |
| 11.4 | 29 | ||
| 15 | 30 | ||
| Medium | 7.6 | 31 | |
| 11.4 | 32 | ||
| 15 | 33 | ||
| High | 7.6 | 34 | |
| 11.4 | 35 | ||
| 15 | 36 | ||
| 5% | Low | 7.6 | 37 |
| 11.4 | 38 | ||
| 15 | 39 | ||
| Medium | 7.6 | 40 | |
| 11.4 | 41 | ||
| 15 | 42 | ||
| High | 7.6 | 43 | |
| 11.4 | 44 | ||
| 15 | 45 | ||
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| (a) |
 |
| (b) |
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| (c) |
| Figure 3. (a) Variation of annual precipitation in Brookings, SD, from 1960 to 1997 and Iowa City, IA, from 1960 to 2001. (b) Illustrates the variation of precipitation (bar plot) and temperature (lines plot) over the year. The weather parameters were shown for Brookings, SD, and Iowa City, IA, in blue and red colors. Dotted and solid lines indicate the minimum and maximum temperature, respectively, and (c) Depicts the variation of mean monthly potential evapotranspiration over the year during study period. |
on expected growth patterns post-harvest for warm and cool season cover crops. Table 2 shows the modified canopy coverage and root depth for the specific cover crop treatments corresponding to the original crop file in figure 2b. In SPAW, canopy cover is considered the surface covered by plant leaves, which directly influences simulated daily evapotranspiration. The cover crop is planted in the corn belt just after harvesting corn and terminated before the following growing season. During fall, cool-season and warm-season cover crops germinate and grow in late fall. Warm-season cover crops winter kill (terminated by freeze), and cool-season cover crops go dormant in spring before reactivating in spring and growing until termination, usually through an herbicide burndown. In warm and cool-season cover crop treatments, canopy cover and rooting depth were increased compared to no cover crop (table 2).
| Table 2. Magnitude of the canopy cover (increase in %) and root depth (added in cm) modification corresponding to the original corn's characteristics to impose the cover crop's evapotranspiration effect during the non-growing season. No cover crop treatment (control) represents the original corn crop file. | |||||||||
| Cover Crop Treatment | Cover Crop Growing Period | ||||||||
| Oct | Nov | Dec | Jan | Feb | Mar | Apr | May | ||
| Warm-season CC | Canopy (increase in %) | 10 | 20 | 20 | 10 | 10 | 10 | 0 | 0 |
| Root depth (add in cm) | 5 | 10 | 10 | 10 | 0 | 0 | 0 | 0 | |
| Cool-season CC | Canopy (increase in %) | 10 | 15 | 20 | 20 | 30 | 40 | 0 | 0 |
| Root depth (add in cm) | 5 | 10 | 10 | 10 | 15 | 35 | 35 | 25 | |
| No CC (control) | Canopy (increase in %) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Root depth (add in cm) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
Statistical Analysis
SPAW models were run at a daily time step for 135 soil system combinations (table 1), including three cover crop management practices and two weather regimes. In total, 810 simulations were run on climate data from two weather regimes, resulting in over three million data points. All data were processed and analyzed using the statistical software R (R Core Team, 2022). Generalized linear models were developed to identify significant parameters to predict the runoff (%) and infiltration (%) generated from the SPAW model. Runoff and infiltration were normalized as a percent of precipitation. The normalized model outputs were used instead of absolute values to account for differences in climate inputs.
Results and Discussion
Water Balance
Runoff and infiltration are major components in field-scale hydrology and play a vital role in nutrient movement and soil erosion, which in turn have a significant impact on down-gradient surface and groundwater. Runoff and infiltration were estimated using SPAW for 38 years (Brookings, SD) and 42 years (Iowa City, IA) of climatological data at daily time steps under different combinations of field conditions. A total of 810 water balances were estimated for two weather regimes with the equal number of model simulations for each region. Model output included precipitation, irrigation, runoff, infiltration, evaporation, transpiration, interception, percolation, deep drainage, and soil moisture change (table 3). Higher precipitation in Iowa City, IA, produced a higher proportion of runoff (8% to 21% of precipitation dependent on soil type) and a lower proportion of infiltration (73% to 60% of precipitation dependent on soil type) compared to Brookings's runoff (4% to 14% of precipitation dependent on soil type) and infiltration (73% to 62% of precipitation dependent on soil type). Upon infiltration, water could be taken up through the plant system, returned to the atmosphere through evaporation directly from the soil surface, or continued through the soil profile as percolation or deep drainage. On average, 63% (Iowa City, IA) and 67% (Brookings, SD) of the water balance was evapotranspiration (excluding interception loss during precipitation). The interception was also a significant portion of the water balance, making up 24% (Iowa City, IA) and 19% (Brookings, SD) of precipitation. Interception is defined as water caught by the plant canopy and the earth’s surface, which subsequently evaporates out of the system (Gerrits, 2010; Mohammad and Adam, 2010).
The following generalized linear models (eqs. 3 and 4) were executed in R (R Core Team, 2022) to identify the variable that has a significant effect on the runoff (%) and infiltration (%) generation process in the SPAW model:
(3)
(4)
where
Weather = precipitation (Brookings, SD, and Iowa City, IA)
Covercrop = cover crops (with cover crop and no-cover crops)
CCType = Cover crop types (Warm-season, cool-season, and no-cover crops)
BD = Compaction level (low, medium, and high)
SL= Thickness of the soil layer (7.6, 11.4, and 15 cm)
Soil = Soil texture (Clay, Sandy loam, and silty loam).
It was found that all but cover crop treatments and their interaction had a significant impact on runoff (%) and infiltration (%), as expected from the conceptualization of agro-hydrology.
Impact of Soil Properties Variation Over Runoff and Infiltration
Runoff and infiltration significantly differed with soil layer thickness (figs. 4a and 4b). Increasing soil layer thickness increases runoff (%) and decreases infiltration (%). Figures 4c and 4d depict that the variation of runoff (10.35%, 10.37%, 10.40%, 10.42%, and 10.46%) and infiltration (68.34%, 68.31%, 68.28%, 68.25%, and 68.22%) was small for the total simulation period because of organic matter levels changing from 1% to 5%, respectively. However, we found that the variations of runoff (%) and infiltration (%) were statistically significant if the organic matter level increased by 2% or more than 2%. In addition, SPAW model simulations showed that increasing the organic matter level increased runoff and decreased infiltration. However, previous field research showed that no-till fields increased organic matter and developed greater porosity and aggregate stability, increasing infiltration and reducing runoff from the field (Rhoton et al., 2002; Wilson et al., 2004). The soil compaction significantly affects runoff (%) and infiltration (%) (figs. 4e and 4f). The SPAW model outcome showed that increasing compaction decreases runoff (%) and increases infiltration (%), which would underestimate field runoff. However, previous research showed that compaction increases runoff and decreases infiltration (Woltemade, 2010; Yang and Zhang, 2011). Therefore, this study indicates that SPAW model has limitations in responding to runoff and infiltration generation due to organic matter and compaction issues, and model improvement is required in the future. In the field experiment, Andersen et al. (2010) also found that the SPAW considerably underestimated the field runoff for hydrologic soil group B, and eventually, modeling results performed unsatisfactorily during model evaluation, having Nash-Sutcliffe efficiency (NSE) value of 0.32 and the percent bias (BIAS) value of 32.
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Soil texture class also affected runoff and infiltration (figs. 4g and 4h). As expected, clay generates much more runoff than the other two soil texture classes. It has less infiltration capacity and allows precipitation to flow overland; thus, it causes a higher runoff (%) and a lower infiltration (%). However, SPAW generated higher runoff (%) and less infiltration (%) for sandy loam compared to silty loam soil, which is unrealistic. Usually, sandy loam has higher infiltration and lower runoff capacity compared to silty loam soil; thus, SPAW model improvements are needed in the future to capture this issue. Figures 5 and 6 show the five-dimensional heat map, respectively representing the runoff (%) and infiltration (%) generated from the system to visualize the effect of soil property changes. It gives a better understanding of SPAW model performance with other soil property changes. While other variables were statistically significant, texture was the dominant factor.
 |
| (a) |
Runoff (%) in water balance |
| (b) |
| Figure 5. Illustration of the impact of soil properties and soil class changes over the runoff (%) in the water balance. Here, the x-axis represents organic matter (%), and the y-axis represents the compaction level. Each row represents the thickness of the topsoil layer (7.6, 11.4, and 15.2 cm), subject to changes in other soil properties (such as compaction levels and % of organic matter). Each column represents the class (clay, sandy loam, and silty loam). (a) runoff (%) variation shows with a single-color scale. (b) runoff (%) variation shows different color scales on the bottom of each column for the respective soil class. |
Figures 5a and 6a show differences in runoff and infiltration across the identical color ramp, and figures 5b and 6b show differences in runoff and infiltration with different scales within each texture. Overall, runoff and infiltration varied from 5% to 19% and 59% to 74% within these two weather regimes, respectively. An increment of topsoil layer thickness increases runoff (%) and decreases infiltration (%). It was found that there was a significant variation of runoff and infiltration generated among soil classes, but there was less or almost no difference within the different topsoil layers' thickness. Moreover, there was little or almost no runoff and infiltration variation within and between the topsoil layer thicknesses due to the change in soil compaction and organic matter (%). To show the variation of runoff (%) and infiltration (%) within the soil class and topsoil thickness, figures 5b and 6b presented the heatmap with a color scale for individual soil texture classes. These figures represent the variation in runoff (%) and infiltration (%) due to increased organic matter and compaction levels. SPAW modeling results showed that the highest runoff (%) was generated in clay soil (16% to 19%), and the lowest was observed in silty loam soil (5.7% to 6.2%) (fig. 5b). In the field experiment, Suryoputro et al. (2017) found higher runoff (%) for clay and lower runoff (%) for sandy loam in both cultivated and pasture land. In the case of infiltration, SPAW simulated the lowest infiltration for clay (59% to 63%) and the highest infiltration in silty loam soil (72.5% to 73.5%) (fig. 6b). Typically, clay soil is more susceptible to seal formation than silty loam, which predominantly reduces infiltration rates and generates higher runoff (Mamedov et al., 2001). In sandy loam soil, runoff and infiltration vary from 7.2% to 8% and 70.6% to 71.4%, respectively.
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| (a) |
Infiltration (%) in water balance |
| (b) |
| Figure 6. Illustration of the impact of soil properties and soil class changes over the infiltration (%) in the water balance. Here, the x-axis represents organic matter (%), and the y-axis represents the compaction level. Each row represents the thickness of the topsoil layer (7.6, 11.4, and 15.2 cm), subject to changes in other soil properties (such as compaction levels and organic matter (%)). Each column represents the class (clay, sandy loam, and silty loam). (a) infiltration (%) variation shows with a single-color scale. (b) infiltration (%) variation shows different color scales on the bottom of each column for the respective soil class. |
Adopting long-term soil health management practices significantly influences the topsoil layer through biological activity and others that would expand the depth of improved soil structure (Doran et al., 1998). See the left column of figures 5b and 6b for the runoff and infiltration variation specifically for clay under the consideration of compaction and organic matter changes. SPAW modeling outcome shows that increasing topsoil layer thickness would have generated a higher runoff (%) and a lower percentage of infiltration (%). Clay soil has a lower infiltration capacity, and increasing the thickness of the clay (topsoil) layer decreases the water flow through it as clay has a lower hydraulic conductivity. Therefore, increased clay topsoil layer thickness produces low infiltration and higher runoff. One potential source of error or unrealistic results was demonstrated by SPAW: increased soil compaction of the clay topsoil results in decreased runoff (%) and increased infiltration (%). Usually, compacted soil has less permeability; thus, compaction decreases infiltration and increases runoff (Alaoui et al., 2018; Ekwue and Harrilal, 2010; Schafer et al., 1992). In the case of organic matter (%) increased, SPAW modeling results showed that there was more runoff (%) and less infiltration (%) for clay. Interestingly, there was no noticeable change in runoff (%) and infiltration (%) for the changes in organic matter level if the compaction level in clay was high. However, a field study found that increasing organic matter enhanced soil structure and increased infiltration, reducing runoff from the field (Boyle et al., 1989; Rhoton et al., 2002; Wilson et al., 2004).
In the case of sandy loam soil (middle column of figs. 5b and 6b), increasing topsoil thickness from 7.6 cm to 11.4 cm or 11.4 cm to 15.2 cm results in a slight increase (about 0.2% to 0.3%) for runoff and a decrease by the same amount (about 0.2% to 0.3%) for infiltration. Increasing compaction produced slightly more runoff (0.1% to 0.2%) and decreased infiltration. Increasing organic matter in sandy loam reduced runoff (0.05% decrease for every 1% organic matter increase) and increased infiltration by the same amount. Organic matter improves soil structure and increases water-holding capacity, reducing runoff; however, this model did not respond to changes in organic matter as expected.
In the case of silty loam soil (right column of figs. 5b and 6b), there was a minor variation across treatments (topsoil layer thickness, compaction level, and organic matter [%]) for percent runoff and percent infiltration. The highest runoff was observed for highly compacted (higher bulk density) soil having higher organic matter (5%) and higher (15.6 cm) topsoil thickness compared to a lower topsoil thickness (7.6 cm) with low compaction and % organic matter. In contrast to the clay soil type, the SPAW model performed conceptually well for simulating runoff (%) and infiltration (%) in sandy loam and silt loam soil.
Impact of Cover Crop (CC) as ACPs on Runoff and Infiltration
We found that the variation of runoff (%) and infiltration (%) within cover crop treatments was statistically significant, but their differences within the treatment were numerically small (fig. 7). The SPAW model simulated that cover crop practices generated higher runoff (10.45%) than without cover crop practices (10.3%) (fig. 7a). In contrast, cover crop practices decreased infiltration by 0.83% in the field compared to the control (fig. 7b). While there is significant evidence from field studies that the implementation of cover crops increases infiltration and consequently decreases runoff from agricultural land (Chalise et al., 2019; Dabney, 1998; Folorunso et al., 1992; Kaspar et al., 2001), this modeling study found no significant difference in runoff generation between warm and cool-season cover types (fig. 8a). However, there was a small but significant increase (0.19%) in the percent of infiltration for the warm-season cover type compared to the cool-season cover crop (fig. 8b). Although warm season cover crops were assumed to have less vigorous growth with respect to canopy coverage and rooting depths due to the cold climate in this study region, the results from these assumed cover crop management scenarios (cool and warm-season cover crop) suggested that the relationship between cover crop practice and runoff-infiltration generation could be more complex and need a more robust approach to modeling by SPAW or another agro-hydrological model than previously thought.
Impact of Climate Scenario on the Generation of Percent Runoff and Infiltration
The precipitation scenario significantly influenced the runoff and infiltration generation in the water balance (fig. 9). In the low precipitation scenario (Brookings, SD), the weather regime produced 5.14% lower runoff and 0.51% higher infiltration than in the high precipitation scenario (Iowa City, IA). In the low precipitation scenario (Brookings, SD), a higher number of low-intensity rainfall events allowed for greater infiltration, resulting in less runoff compared to the higher precipitation scenario (Iowa City, IA).
Figures 10 and 11 show the jitter plot, which depicts the influences of precipitation scenarios over the percent of the water balance that became runoff and infiltration under individual soil property changes and cover crop management practice effects. The runoff and infiltration patterns indicated that there were considerable influences from the weather regime (figs. 10a-d). Runoff (%) was always higher in the Iowa City, IA, weather regime compared to Brookings, SD, for all treatments (topsoil thickness, organic matter, compaction level, and cover crop type), indicating that precipitation characteristics play a dominant role in the runoff and infiltration portions of the water balance. There was a positive correlation between percent runoff and topsoil thickness, organic matter level, and adoption of cover crop practices, and a negative correlation between runoff and compaction. There was significant variability across other treatments within the clay texture class, but very small or almost no variability across other treatments for sandy loam and silty loam soil. More specifically, within weather regimes (Brookings or Iowa City), there is almost no variability observed for sandy loam and silty loam soil across different organic matter percentages. We found that the runoff (%) was reduced considerably for the highly compacted soil in Iowa City, IA, compared to other compaction levels. It is unclear what physical mechanism within the model is driving this, but it reinforces the potential sensitivity by SPAW to changes in bulk density. The cover crop has little impact relative to no cover crop practice on runoff (%) within the soil class and weather regime.
Like runoff, the maximum variability of infiltration was also observed for clay soil for both weather regimes, but the variability of infiltration across other treatments was very small or negligible for sandy loam and silty loam soils. There was a negative correlation between infiltration and topsoil thickness, organic matter level, and adoption of cover crop practice, but a positive correlation with compaction level. The high precipitation regime in Iowa City, IA, greatly influenced the percent infiltration by soil texture class. The percent of the water balance that became infiltration was consistently high for silty loam soil and low for sandy loam and clay soil relative to Brookings, SD (figs. 11a-d).
 |
| Figure 11. Jitter plot illustrates the impact of soil properties and cover crop management practice in different precipitation regimes over infiltration (%) generation in water balance. (a) Topsoil thickness, (b) Organic matter, (c) Compaction level, and (d) Cover crop. Warm= Warm seasonal cover crop, Cool = Cool seasonal cover crop, and No CC= no cover crop (control). The color in the legend indicates the individual soil classes. |
Interestingly, there was no variability for infiltration for sandy loam and silty loam soils due to changes in organic matter for either weather regime. However, infiltration was reduced considerably in clay soil due to the increase in organic matter. The lowest infiltration was observed for the low compacted soil in Iowa City, IA, compared to other compaction levels or within weather scenarios. And the inclusion of cover crops increased runoff within the soil class and weather regime, but there was no difference between cover crop types.
Conclusions
The synthetic SPAW model confirmed that soil properties change over time with ACPs, implementation of cover crops, and precipitation scenarios that significantly affect runoff and infiltration generation, which demonstrates the applicability and sensitivity of SPAW under varying scenarios with limitations. More specifically, soil texture, compaction levels, and topsoil layer thickness for clay substantially influence the field runoff (%) and infiltration (%) generation than silty loam and sandy loam. SPAW model results show that increasing the organic matter level (1% to 5%) does not substantially affect runoff or infiltration within silty and sandy loam soil. According to the model output, cover crop treatment slightly increased runoff but was statistically significant compared to no cover crop treatment. This seems to be a limitation of the model since literature suggests that implementation of this ACP should result in reduced runoff. A low precipitation climate scenario generates a significantly lower runoff (%) in the climatic scenario, and eventually, it will increase the infiltration percent of precipitation. Finally, we can conclude that the SPAW model is sensitive but has some limitations in modeled field runoff and infiltration for potential soil property changes due to the implementation of ACPs, especially in the clay soil class, soil compaction, and organic matter change.
Climate and soil texture influenced runoff and infiltration more than any other variables. This indicates that the effect on the water balance of changes to soil properties from soil health practices is likely minimal when compared to factors largely beyond our local control, such as soil texture and precipitation.
While variables and treatments for this modeling experiment were selected from literature values, there were limitations to the model and approach. Some unexpected results, such as lower runoff (%) for higher compaction in some cases and higher runoff (%) for increasing organic matter and cover crop practices, indicate that some calibration and validation are needed. This reinforces the need for this study because no calibration is typically performed for the use of SPAW in permitting applications (for CAFOs), indicating there could be some error in runoff estimations used in permitting scenarios. Also, while changes to the entire water balance can be modeled for forced variables, these are prescribed by the user rather than as a result of changes to practices. For example, changes to bulk density can be forced, but it is up to the user to determine how bulk density would change due to various management practices such as fewer field passes, conservation tillage, or manure application. Since these variables are not readily available for most SPAW users, they may be assigned using possibly suspect judgment, or not modified at all.
Acknowledgments
We would like to acknowledge financial support from the USDA-NRCS (Conservation Collaboration Grant/Agreement - Grant contribution No. NR186740XXXXG007), USGS (104b mini-grant), Ducks Unlimited, and NSF(grant CNS-2202706). We also acknowledge USDA-ARS for providing free downloads and access to the SPAW model and data sets.
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