Modeling phosphorus in the environment - Chapter 9

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Ô nhiễm nông nghiệp theo năm Nonpoint Nguồn (AnnAGNPS) là một mô hình công cụ đánh giá đầu nguồn công nghệ tiên tiến đã được phát triển thông qua một quan hệ đối tác giữa hai Bộ Nông nghiệp (USDA) cơ quan Hoa Kỳ - Dịch vụ Nghiên cứu Nông nghiệp (ARS) và Tài nguyên Dịch vụ Bảo tồn thiên nhiên (NRCS) - để hỗ trợ trong việc đánh giá phản ứng đầu nguồn để quản lý nông nghiệp thực hành (Bingner và Theurer 2001). AnnAGNPS là một mô phỏng liên tục, hàng ngày bước thời gian, tải gây ô nhiễm mô hình được thiết kế...

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  1. 9 Phosphorus Modeling in the Annualized Agricultural Nonpoint Source Pollution (AnnAGNPS) Model Yongping Yuan U.S. Department of Agriculture-Agricultural Research Service, Oxford, MS Ronald L. Bingner U.S. Department of Agriculture-Agricultural Research Service, Oxford, MS Indrajeet Chaubey University of Arkansas, Fayetteville, AR CONTENTS 9.1 Model Introduction ......................................................................................216 9.2 Watershed Processes Considered in AnnAGNPS........................................216 9.3 Model Inputs and Outputs ...........................................................................217 9.4 AnnAGNPS Model of Phosphorus Processes .............................................219 9.4.1 Soil Initial Phosphorus Content.......................................................220 9.4.2 Organic P Simulation Processes ......................................................221 9.4.3 Inorganic P Simulation Processes....................................................222 9.4.3.1 Calculation of Inorganic P Additions to a Cell ...............222 9.4.3.2 Calculation of Intermediate Inorganic P Mass Balance..................................................................223 9.4.3.3 Calculation of Inorganic P Losses from the Soil Profile .........................................................224 9.4.4 Total Runoff Losses .........................................................................226 9.5 Model Application........................................................................................226 9.5.1 Study Watershed and Monitoring Information................................226 9.5.2 Input Data Preparation .....................................................................227 215 © 2007 by Taylor & Francis Group, LLC
  2. 216 Modeling Phosphorus in the Environment 9.5.3 Sensitivity Analysis..........................................................................229 9.5.4 Model Calibration and Validation....................................................232 9.6 Model Limitations........................................................................................238 9.7 Conclusions ..................................................................................................238 References..............................................................................................................238 9.1 MODEL INTRODUCTION The Annualized Agricultural Nonpoint Source Pollution (AnnAGNPS) model is an advanced technological watershed evaluation tool that has been developed through a partnership between two U.S. Department of Agriculture (USDA) agencies — the Agriculture Research Service (ARS) and the Natural Resources Conservation Service (NRCS) — to aid in the evaluation of watershed responses to agricultural management practices (Bingner and Theurer 2001). AnnAGNPS is a continuous-simulation, daily time-step, pollutant loading model designed to simulate long-term chemical and sedi- ment movement from agricultural watersheds (Bingner et al. 2003). The spatial vari- ability of soils, land use, and topography within a watershed is accounted for by dividing the watershed into many user-specified, homogeneous, drainage-area-determined cells. For individual cells, runoff, sediment, and pollutant loadings can be predicted from precipitation events that include rainfall, snowmelt, and irrigation. Each day, AnnAGNPS simulates runoff, sediment, nutrients, and pesticides leaving the land surface and being transported through the watershed channel system to the watershed outlet before the next day is considered. The model routes the physical and chemical constituents from each cell into the stream network and finally to the watershed outlet and has the capability to identify the sources of pollutants at their origin and to track them as they move through the watershed system. The AnnAGNPS model has evolved from the original single-event Agricultural Nonpoint Source (AGNPS) model developed in the early 1980s by the USDA-ARS (Young et al. 1989, 1995). The AGNPS model was developed to simulate runoff and water-quality response of agricultural watersheds ranging from a few hectares to 20,000 hectares from a single rainfall event. The AGNPS model has been applied throughout the world to investigate various water quality problems. The AnnAGNPS model includes significantly more advanced features but retains many of the impor- tant features of AGNPS. (The complete suite of AnnAGNPS model, composed of programs, pre- and post-processors, technical documentations, and user’s manuals, is currently available at http://www.ars.usda.gov/Research/docs.htm?docid=5199.) 9.2 WATERSHED PROCESSES CONSIDERED IN AnnAGNPS The hydrology components considered within AnnAGNPS are rainfall, interception, runoff, evapotranspiration (ET), infiltration/percolation, subsurface lateral flow, and sub- surface drainage. The runoff from each cell is calculated using the Soil Conservation Service (SCS) curve number (CN) method (Soil Conservation Service 1985). The mod- ified Penman equation (Jenson et al. 1990; Penman 1948) is used to calculate the potential ET, and the actual ET is represented as a fraction of potential ET. The fraction is a linear © 2007 by Taylor & Francis Group, LLC
  3. Phosphorus Modeling 217 function of soil moisture between wilting point and field capacity. For percolation, only the downward drainage of soil water by gravity is calculated (Bingner et al. 2003). Lateral flow is calculated using Darcy’s equation, and subsurface drainage is calculated using Hooghoudt’s equation (Freeze and Cherry 1979; Smedema and Rycroft 1983). Amount of sheet and rill soil erosion loss — not field deposition — for each runoff event is calculated using the Revised Universal Soil Loss Equation (RUSLE) model (Renard et al. 1997). A delivery ratio, which quantifies the amount of sediment deposited in the field and the amount of sediment delivered to the stream, is calcu- lated using the Hydrogeomorphic Universal Soil Loss Equation (HUSLE) model (Theurer and Clarke 1991). Ephemeral gully erosion is based on the Ephemeral Gully Erosion model (Merkel et al. 1988). The model uses the Bagnold equation (Bagnold 1966) to determine the sediment transport capacity of the stream and a modified Einstein equation to determine the sediment transport in the stream system (Bingner et al. 2003). Sediment is partitioned into five classes: clay, silt, sand, small aggregates, and large aggregates. The model estimates particle-size distribution of deposited sediment by taking into account the density and fall velocity of each class. The AnnAGNPS model calculates a daily mass balance within each cell for soil moisture, nitrogen (N), phosphorus (P), organic carbon (OC), and pesticides. Plant uptake of nutrients, fertilization, residue decomposition, mineralization, and trans- port are major factors considered to determine the fate of nutrients in the watershed. Both soluble and sediment adsorbed nutrients are considered by the model. The pesticide component is adopted from the Groundwater Loading Effects of Agricultural Management Systems (GLEAMS) model (Leonard et al. 1987). The AnnAGNPS model allows simulation of any number of pesticides and treats each pesticide separately with independent equilibrium assumed for each pesticide. Both soluble and sediment-adsorbed fractions of each pesticide are calculated on a daily time scale. Factors affecting fate and transport of pesticides include foliage wash- off, vertical transport in the soil profile, and degradation. 9.3 MODEL INPUTS AND OUTPUTS A complete list of AnnAGNPS input data sections is shown in Figure 9.1. These data can be grouped into the following categories: climate, watershed physical information, land-management operations, chemical characteristics, and feedlot operations. Daily precipitation, maximum and minimum temperatures, dew point temperature, sky cover, and wind speed are climate data required by the model to perform continuous simulation. Climate data used with AnnAGNPS can be histor- ically measured, synthetically generated using the climate generator program (Johnson et al. 2000), or a combination of the two. Geographic information systems (GIS) data layers of a watershed are needed to characterize the watershed. The GIS data layers must be in sufficient spatial detail to permit the model to accurately reflect the real landscape it represents. Using the GIS layers of digital elevation model (DEM), soils, and land use, a majority of the large data input requirements can be developed using a customized ArcView GIS interface. Those input requirements include watershed and cell delineation, cell land slope, slope direction, cell land use and soil type, and stream reach data, can be © 2007 by Taylor & Francis Group, LLC
  4. 218 AnnAGNPS Watershed Simulation Daily Verification Global Identifier Data Period Climate Data Output Field Pond Point Feedlot Feedlot Gully Field Pond Management Source Management Cell Data Reach Data Management Tile Drain Soils Field Reach Channel Reach Nutrient Impoundment Geometry Half Life Modeling Phosphorus in the Environment Management Schedule Runoff Curve Fertilizer Pesticides Management Strip Irrigation Contours Crop Number Application Application Operation Crop Fertilizer Pesticides Non-Crop Reference Reference Required Required if Referenced Optional FIGURE 9.1 A complete list of AnnAGNPS input data sections. © 2007 by Taylor & Francis Group, LLC
  5. Phosphorus Modeling 219 developed by using a customized ArcView GIS interface. Additional input require- ments, which include developing the soil layer attributes to supplement the soil spatial layer, describing crop operations and management practices, defining channel hydraulic characteristics, and entering many other optional data sections as needed by the watershed (Figure 9.1), can be organized using the AnnAGNPS Input Editor. The Input Editor is a graphical user interface developed to aid users in selecting appropriate input parameters. Much of the information needed to characterize crop characteristics, field operations (e.g., crop rotation, tillage, planting, harvesting), chemical characteristics, feedlots, and soils can be obtained from databases imported from RUSLE or from other USDA-NRCS data sources. Feedlot information includes daily manure production rates, manure character- istics, amount of manure removed from the field lot, and residual amount of manure available from previous operations. The model outputs include runoff, sediment, nutrient, and pesticide at a temporal scale ranging from daily to yearly. All model outputs can be obtained at any desired location such as specific cells, stream reaches, feedlots, gullies, or point sources. The model also has capabilities to provide source accounting information in terms of the fraction of a pollutant loading passing through any reach location that originated from a user-specified pollutant source area. Cronshey and Theurer (1998), Geter and Theurer (1998), and Theurer and Cronshey (1998) provide detailed information on available model outputs. 9.4 AnnAGNPS MODEL OF PHOSPHORUS PROCESSES Simulation of P transport and transformation processes at a watershed scale is very challenging because of the complexities and uncertainties related to the processes. A complete understanding of the relationship of various P pools and their chemical, physical, and biological interactions in the soil profile is essential for a full descrip- tion of the P cycle in soils and plants (Jones et al. 1984). A model based on mathematical descriptions of fundamental chemical, physical, and biological mech- anisms of the soil P behavior would be ideal for P modeling. In general, the chemical component in AnnAGNPS exists in two phases: dis- solved (solution) in the surface runoff and attached (adsorbed) to clay-size particles resulting from sheet and rill erosion. To simulate P loading, daily soil mass balances of P in a cell are maintained for each computational layer. The daily mass balances of P are adapted from the Erosion Productivity Impact Calculator (EPIC) model (Sharpley et al. 1984; Sharpley and Williams 1990). The P processes simulated in AnnAGNPS are shown in Figure 9.2. More specifically, P is partitioned into inorganic P and organic P, and a separate mass balance is maintained for each. Inorganic P is further broken down into (1) labile P, or P readily available for plant uptake; (2) active P, or P that is more or less reversibly adsorbed to the soil; and (3) stable P, or adsorbed P that is fixed or relatively irreversibly chemisorbed to the soil adsorption complex or as discrete insoluble P minerals. The model simulates the effect of P adsorption that controls P availability to plant uptake and runoff loss, and the model also simulates P movements between labile P and active P and between active P and stable P. Sediment-attached © 2007 by Taylor & Francis Group, LLC
  6. 220 Modeling Phosphorus in the Environment Inorganic Organic Organic Inorganic Erosion Plant Runoff Erosion Plant residue fertilizer uptake fertilizer loss loss loss Mineralization Desorption Active and Active Solution Stable Decay (Humic) Adsorption Residue mineralization Fresh Stable FIGURE 9.2 Phosphorus processes simulated in AnnAGNPS. P estimated from soil erosion is assumed to be associated with the clay-size fraction of the soil and consists of both organic and inorganic P. Major processes considered are residue decomposition and mineralization, fertilizer application, plant uptake, runoff, and erosion losses. Plant uptake of P is modeled through a simple crop-growth stage index either specified by the user or by the model (Bingner et al. 2003). Phosphorus losses from each AnnAGNPS cell within a stream reach are added to an AnnAGNPS reach. Phosphorus is reequilibrated between dissolved P and sediment-attached P in the reach during transport to the watershed outlet. 9.4.1 SOIL INITIAL PHOSPHORUS CONTENT The initial soil P content is needed to initialize AnnAGNPS simulation. Usually, calibration is recommended to define the initial soil P content. The input P levels in the soil profile are input as concentrations, but AnnAGNPS performs calculations on a mass basis. To convert a concentration to a mass, AnnAGNPS uses a conversion factor, conv (Equation 9.1). The conversion factor converts nutrient concentration in soil to mass (in kilograms) using Equation 9.1: conv = 10,000 ρ b D Acell (9.1) where conv is the intensive unit-to-extensive-unit conversion factor (kg), ρb is the bulk density of composite soil layer (g/cm3 or mg/ m3), D is thickness of soil layer (mm), and Acell is the AnnAGNPS cell area (ha). © 2007 by Taylor & Francis Group, LLC
  7. Phosphorus Modeling 221 9.4.2 ORGANIC P SIMULATION PROCESSES All AnnAGNPS mass balances are based on AnnAGNPS cells and are maintained for two composite soil layers. The first soil layer is 203 mm in depth from the surface, typically defined as the tillage layer by RUSLE. The second soil layer is from the bottom of the tillage layer to either an impervious layer or the user-supplied depth of the soil profile. The mass balance equation for organic P simulation is as follows: (resP + orgPfer − hmnP − orgPsed ) 1,000,000 orgPt = orgPt −1 + (9.2) conv where orgPt is organic P concentration in the composite soil layer for the current day (mg/kg), orgPt–1 is organic P concentration in the composite soil layer for the previous day (mg/kg), resP is organic P addition to a cell from decomposed fresh crop residue (kg), orgPfer is organic P addition to a cell from fertilizer application (kg), hmnP is the mineralization from the humus active organic P pool (kg), and orgPsed is organic P loss from a cell by attaching to sediment (kg). Decomposition is calculated once a day. Equations for residue decomposition were adapted from RUSLE. Only surface decomposition is calculated for crop land. Cell organic P from fertilizer application is the product of the fertilizer applied for the current day and the organic P fraction in the fertilizer. The organic P fraction can be obtained from the fertilizer reference database in AnnAGNPS. The P mineralization equation is adapted from the EPIC model (Sharpley and Williams 1990). Temperature and aeration, represented by soil moisture, are con- sidered for P mineralization (Sharpley and Williams 1990). AnnAGNPS assumes that organic phosphorous is associated with the clay fraction of the soil. Sediment- attached organic P is calculated by Equation 9.3: orgPsed = forgP × sed clay × 1000 (9.3) where forgP is a decimal fraction of organic P in clay in soil layer (g/g), and sedclay is the amount of clay in the mass of sediment (mg). The decimal fraction of organic P is: orgP forgP = (9.4) fclay × 1,000,000 where orgP is the organic P concentration in the composite soil layer (mg/kg), and fclay is the fraction of clay to total composite soil, provided by the soil database. Organic P mass balance is maintained for the second soil layer the same way as the first layer except that fertilizer application and rainfall-induced runoff and sediment loss are not considered. AnnAGNPS assumes that fertilizer application, rainfall-induced runoff, and sediment loss are associated only with the top soil layer. Equation 9.5 represents the mass balance for the second layer: hmnP 1,000,000 orgPt = orgPt −1 − (9.5) conv © 2007 by Taylor & Francis Group, LLC
  8. 222 Modeling Phosphorus in the Environment 9.4.3 INORGANIC P SIMULATION PROCESSES AnnAGNPS simulates three different pools of inorganic P in the soil. It adapts the principles of the soil mineral P model developed by Jones et al. (1984). Mineral P is transferred among three forms: labile P in solution (available for plant use and runoff loss), active P, and stable P. AnnAGNPS assumes that inorganic P added from fertilizers initially goes to the labile P pool and the active P pool, based on a value of the P sorption coefficient. Fertilizer P that is labile at application may be quickly transferred to the active mineral pool. Many studies have shown that after an appli- cation of inorganic P fertilizer, solution P concentration in the soil decreases rapidly with time due to reaction with the soil. This initial fast reaction is followed by a much slower decrease in solution P that may continue for several years (Barrow and Shaw 1975; Munns and Fox 1976; Rajan and Fox 1972; Sharpley 1982). Flow between the active and stable mineral pools is governed by a P exchange rate. Within each inorganic P pool, addition from fertilizer application is calculated first, followed by the mineralization of organic P. Then, losses through runoff, erosion, and plant uptake are calculated. At the end of each day, the mass balance is updated for each P pool. The simulation is a sequence of adjusting the mass balance of each inorganic P pool. 9.4.3.1 Calculation of Inorganic P Additions to a Cell Fertilizer additions are simulated in one of two ways: well mixed with the top soil layer or unincorporated on the soil surface. On a daily basis, AnnAGNPS checks if there is a tillage operation and the percentage of soil disturbance from the tillage operation. If the soil disturbance exceeds 50% of the top soil layer, any fertilizer applications are considered as mixed. Otherwise, it assumes the applied fertilizer stays on the soil surface. In addition, when the soil disturbance exceeds 50% of the soil, it incorporates not only the applied fertilizer on the current day but also any fertilizer left on the soil surface from previous applications. Therefore, when soil disturbance exceeds 50% of the top soil layer, mnaP = surf _ inorgP (9.6) where mnaP is the mass of inorganic P added to the soil profile from the current operation (kg) (and it is assumed to be well mixed with the first soil layer), and surf_inorgP is the surface inorganic P in a cell, added through fertilization at the soil surface (kg). If a fertilizer is applied in the current operation, then mnaP = mnaP + inorgPfer (9.7) where inorgPfer is inorganic P applied during the current operation (kg). It is calcu- lated using the rate of fertilizer applied for the current day times the inorganic P fraction (from the fertilizer reference database mass/mass). When soil disturbance is less than 50% of the soil, the fertilizer on the soil surface remains on the soil surface and nothing is incorporated into the soil profile. If a fertilizer is applied for the current operation, then surf _ inorgP = surf _ inorgP + inorgPfer (9.8) © 2007 by Taylor & Francis Group, LLC
  9. Phosphorus Modeling 223 Then, AnnAGNPS checks if a rainfall event occurred, and if so, soil inorganic P is adjusted to reflect the rainfall impact. When a rainfall event occurs, it dissolves the soluble P on the soil surface. When the rainfall generates runoff, AnnAGNPS assumes that inorganic P on the soil surface is totally dissolved in the water and is either carried away with runoff or is carried into the soil profile with infiltration. The amount of inorganic P carried away with runoff or carried into the soil profile with infiltration is determined based on the amount of runoff and infiltration from the rainfall event. Q surf _ sol _ P = surf _ inorgP (9.9) (Q + inf ) inf inf _ sol_ P = surf _ inorgP (9.10) (Q + inf ) where surf_sol_P is mass of inorganic P in runoff (kg), inf_sol_P is the amount of inorganic P carried into the soil profile by infiltration (kg), Q is the amount of surface runoff (mm), and inf is the amount of infiltration (mm). Then, the amount of inorganic P carried into the soil profile by infiltration is added to the mnaP value to reflect the impact of the current rainfall event. 9.4.3.2 Calculation of Intermediate Inorganic P Mass Balance The intermediate inorganic P mass balance refers to P pools with P additions but prior to any P losses to runoff, erosion, and plant uptake. Bottom soil-layer inorganic P does not change with this operation. A portion of the incorporated inorganic P is added into the labile P pool: Psp mnaP 1,000,000 labPi = labPstart − mpr + (9.11) conv where labPi is the concentration of intermediate labile inorganic P in the composite soil layer (mg/kg), labPstart is the concentration of labile inorganic P at the beginning of a day, and it is equal to the labile P at the end of the previous day (mg/kg), mpr is the flow rate of P between labile and active P pools on the current day (+ implies flow from labile to active pool; – implies flow in the opposite direction) (mg/kg/d) (Sharpley and Williams 1990), Psp is the soil type-dependent P sorption coefficient (dimensionless) (Sharpley and Williams 1990), and mnaP is mass of inorganic P added to a cell soil profile (kg). The rest of the incorporated inorganic P is added into the active P pool: (1 − Psp) mnaP 1,000,000 actPi = actPstart + mpr + − aspr (9.12) conv © 2007 by Taylor & Francis Group, LLC
  10. 224 Modeling Phosphorus in the Environment where actPi is the concentration of intermediate active inorganic P in the composite soil layer (mg/kg), actPstart is the concentration of active inorganic P at the beginning of a day (equal to the active P at the end of the previous day) (mg/kg), and aspr is the flow rate of P between active and stable P pools on the current day (+ implies flow from active to stable pool; – implies flow in the opposite direction) (mg/kg/d) (Sharpley and Williams 1990). Stable P pool size is calculated as follows: stbPi = stbPstart + aspr (9.13) where stbPi is concentration of intermediate stable inorganic P in the composite soil layer (mg/kg) and stbPstart is the concentration of stable inorganic P at the beginning of a day (equals to the stable P at the end of the previous day) (mg/kg). Then, the inorganic P pools are further adjusted to add the organic P from mineralization. This mineralized P is partitioned among three inorganic P pools based on the fraction of each inorganic P pool to total inorganic P. hmnP flab 1,000,000 labPi+1 = labPi + (9.14) conv hmnP × fact 1,000,000 actPi+1 = actPi + (9.15) conv hmnP × fstb 1,000,000 stbPi+1 = stbPi + (9.16) conv where hmnP is the mineralization from the humus active organic P pool in the soil layer on the current day (kg), flab is the fraction of labile P to total P (total P is the sum of labile P, active P, and stable P), fact is the fraction of active P to total P, and fstb is the fraction of stable P to total P. 9.4.3.3 Calculation of Inorganic P Losses from the Soil Profile This calculation includes sequential adjustments to the P pool size to reflect losses from a cell. 9.4.3.3.1 Loss through Surface Runoff When a rainfall event occurs, runoff interacts with soil and carries soluble inorganic P in the soil profile away from fields. AnnAGNPS assumes the effective depth of runoff interaction with soil to be 10 mm. All soluble inorganic P in the top 10 mm of soil is carried away by the runoff. Soil soluble inorganic P in the top soil layer available for runoff loss is calculated as labP soil_ sol_ inorgP = (9.17) (1 + Kd _ inorgP) © 2007 by Taylor & Francis Group, LLC
  11. Phosphorus Modeling 225 where soil_sol_inorgP is the concentration of soluble P available for runoff loss in a cell soil profile on the current day (mg/kg) and Kd_inorgP is the linear partitioning coefficient for inorganic P (the ratio of the mass of adsorbed P to the mass of P in solution). Soluble inorganic P removed by runoff from the top 10 mm of soil is calculated as soil _ sol _ inorgP Conv cell_ soil_ sol_ inorgP = edi (9.18) Depth 1,000,000 where cell_soil_sol_inorgP is the inorganic P removed from the top soil layer through runoff (kg), edi is the effective depth of interaction factor, AnnAGNPS uses 10 mm, and depth is the depth of the top soil layer (mm). The labile P pool is adjusted to reflect the loss to surface runoff. cell_ soil_ sol_ inorgP 1,000,000 labPi+ 2 = labPi+1 − (9.19) conv 9.4.3.3.2 Loss to Soil Erosion Soil erosion also carries inorganic P away from fields. The inorganic P loss through erosion is calculated the same way as organic P. AnnAGNPS assumes that the inorganic P is also associated with clay fraction. The amount of sediment-attached inorganic P is calculated first; then it is partitioned between the active and stable P pools based on the amount of each pool. sed _ inorgP _ actP 1,000,000 actPi+ 2 = actPi+1 − (9.20) conv sed _ inorgP _ stbP 1,000,000 stbPi+ 2 = stbPi+1 − (9.21) conv where sed_inorg_actP is the sediment loss from active P (kg) and sed_inorg_stbP is the sediment loss from stable P (kg). 9.4.3.3.3 Loss through Plant Uptake of Inorganic P In AnnAGNPS, the amount of crop nutrient uptake is calculated in a crop-growth stage subroutine that determines the crop-growth stage based on crop data specified by a user. Four growth stages —initial, development, mature, and senescence — are simulated by AnnAGNPS. The length of each growth stage can be specified by a user or by the model (Bingner et al. 2003). The amount of nutrient uptake is calculated based on the crop-growth stage and differs by growth stage. The crop nutrient uptake is also limited by available nutrients in the composite soil layer. The calculated crop uptake P in the crop-growth stage subroutine affects the inorganic P mass balance. Phosphorus uptake on a given day is calculated as follows: growth_ P_ uptake yield P _ uptake_ harvest uptP = Acell (9.22) stage_ length © 2007 by Taylor & Francis Group, LLC
  12. 226 Modeling Phosphorus in the Environment where uptP is the amount of inorganic P taken up by the plant on the current day (kg), growth_P_uptake is the fraction of P uptake for the current growth stage, yield is the yield at harvest (kg/ha), P_uptake_harvest = P uptake per yield unit at harvest (mass of P/mass of harvest unit, dimensionless), and stage_length is the the number of growing days for the current growth stage (days). Plant P uptake is adjusted based on the availability of P in the soil. If uptP calculated in Equation 9.22 is greater than the available labile P in the soil layer, then a limited crop P uptake is calculated as labPi+ 2 conv uptPlimited = 0.99 (9.23) 1,000,000 where uptPlimited is the mass of labile P taken up by the plant on the current day (kg) and labPi+2 is the labile P concentration in the soil (mg/kg). The mass of crop uptake P is subtracted from the labile P pool at the end of each day. 9.4.4 TOTAL RUNOFF LOSSES The total mass of inorganic P lost through surface runoff is composed of loss from the soil profile (Equation 9.18) and loss from the soil surface (Equation 9.9). Due to the low mobility of P, leaching loss of soluble P is not simulated. Phosphorus losses from each AnnAGNPS cell within a stream reach are added to an AnnAGNPS reach. Phosphorus is reequilibrated between dissolved P and sediment-attached P in the reach based on the P partitioning coefficient during the process of being transported to the watershed outlet. Detailed P transformation in the reach is not simulated. 9.5 MODEL APPLICATION AnnAGNPS is currently utilized in many locations of the U.S. by the Environmental Protection Agency (EPA), NRCS, and others to estimate the impact of best man- agement practices on nonpoint pollution (Yuan et al. 2002). Several studies have been performed to evaluate the performance of AnnAGNPS in predicting runoff, sediment, and nitrogen losses (Baginska et al. 2003; Suttles et al. 2003; Yuan et al. 2001, 2003). Suttles et al. (2003) evaluated AnnAGNPS performance on P simulation in a coastal plain agricultural watershed in Georgia, and Baginska et al. (2003) performed a similar evaluation on a small experimental catchment in the Sydney region of Australia. This section presents the AnnAGNPS application to the Deep Hollow (DH) watershed and evaluates the performance of AnnAGNPS on P simulation using comparisons with measurements from the DH watershed of the Mississippi Delta Management Systems Evaluation Area project (MDMSEA). 9.5.1 STUDY WATERSHED MONITORING INFORMATION AND Data collected at the DH watershed by Yuan et al. (2001) were used to evaluate the performance of the AnnAGNPS P component. The DH watershed, located in Leflore County, Mississippi, is one of three watersheds studied in the MDMSEA, which seeks to develop and assess alternative innovative farming systems for improved © 2007 by Taylor & Francis Group, LLC
  13. Phosphorus Modeling 227 water quality and ecology in the Mississippi Delta. The main crops grown in the DH watershed are cotton and soybeans. The watershed contains 15 soil series varying in texture from loamy sand to silty clay, but three series cover 80% of the total area (Yuan et al. 2001). Detailed records of agricultural operations including tillage, planting, harvesting, fertilization, cover crop planting, and pesticide usages have been maintained since 1996 (Yuan et al. 2001). A rate of 72.9 kg/ha phosphate fertilizer was applied to cotton fields on October 6, 1998, with equipment that knifes in the material at a depth of 100 mm without further mixing with soil. No fertilizer was applied to soybean fields or during the winter wheat cover crop-growth period. In the period of 1995 to 1996, the U.S. Geological Survey (USGS) installed a gauging station to monitor runoff, sediment, nutrient, and pesticide loadings at one of the inlets to the DH Lake (Yuan et al. 2001). Data collected at this monitoring site were used for this study. The drainage area for the monitored site was 11 ha. Runoff was monitored using a critical flow flume. Both discrete and composite samples were taken during rainfall events for sediment and nutrient analyses. Rainfall was monitored at the flume using a tipping bucket rain gauge. Total P and orthophosphate concentrations were determined for water samples. Total P and orthophosphate mass loads were calculated by using discrete samples when available (Rebich 2004). Loads were also calculated by using composite samples for runoff events when discrete samples were not available (Rebich 2004). 9.5.2 INPUT DATA PREPARATION Established input files for model runoff and sediment evaluation — watershed topo- graphy, soil type, climate data, and actual field operations and management (Yuan et al. 2001) — were modified for this study. Yuan et al. (2001) described the development of input information for AnnAGNPS simulations (complete informa- tion on input file preparation can be found at the AGNPS website at http://www.ars.usda.gov/ Research/docs.htm?docid=5199). The subwatersheds (AnnAGNPS cells), land use, soil information, and stream network for the monitoring site are presented in Figure 9.3. Based on this input file, fertilizer application was timed according to actual field records. Fertilizer application reference information was set up based on AnnAGNPS guidelines and databases. Detailed soil information was obtained from the Soil Survey Geographic (SSURGO) Database (Natural Resources Conservation Service 2005). SSURGO provides most of the soil parameters needed for AnnAGNPS simulation, such as soil texture, erosive factor, hydraulic properties, pH value, and organic matter. However, information on soil nutrient contents was not available from this database. Determining initial soil nutrient values needed for the model was a very difficult task. Soil testing is one way of gaining soil nutrient values. Location, timing, and method of sampling impact the nutrient values obtained from soil testing (Self and Soltanpour 2004). However, soil testing may not be a feasible way to gain soil nutrient values at a watershed scale because of limited resources. First, a watershed may include thousands of fields. Second, each field has different soil types and field managements. Third, nutrient level may vary from one location to another within a field. Consequently, obtaining representative values for the watershed is challenging. © 2007 by Taylor & Francis Group, LLC
  14. 228 Modeling Phosphorus in the Environment Stream location 41 42 Gauging station 43 32 52 51 33 53 62 61 22 63 23 Cell ID Area Soil Type Hydrologic Land Use (ha.) Soil Group 22 0.62 284B Tensas silty clay loam D Soybeans 23 2.2 284B Tensas silty clay loam D Cotton 32 0.32 284B Tensas silty clay loam D Soybeans 33 0.39 284B Tensas silty clay loam D Soybeans 41 2.85 178A Dundee loam C Cotton 42 0.53 284B Tensas silty clay loam D Soybeans 43 0.94 284B Tensas silty clay loam D Cotton 51 0.53 164B Dubbs very fine sandy loam B Cotton 52 0.12 12A Alligator clay D Cotton 53 0.59 178A Dundee loam C Cotton 61 0.81 178A Dundee loam C Cotton 62 0.24 12A Alligator clay C Soybeans 63 1.15 284B Tensas silty clay loam D Cotton FIGURE 9.3 Subwatersheds (cells), land use, soil information, and stream network for the monitoring site. (From Y. Yuan, R.L. Bingner, and R.A. Rebich, Trans. ASAE 44(5), 1183–1190, 2001. With permission.) Therefore, a sensitivity analysis is needed to identify how initial soil nutrient levels impact the simulation result. Literature searches have found that total P in surface soils ranges from 50 to 1500 mg/kg and decreases with depth (Havlin et al. 1999). Organic P typically varies between 15 and 90% of the total P in soils. Thus, as base values for sensitivity analysis, initial soil organic P content was set to 500 mg/kg for the top layer and 250 mg/kg for the subsequent layers; initial soil inorganic P content was set to 250 mg/kg for all soil layers. Detailed crop information such as crop yield, growth period, and amount of residue produced was imported from the RUSLE crop database. However, plant nutrient uptake information was not available from this database. Determining plant © 2007 by Taylor & Francis Group, LLC
  15. Phosphorus Modeling 229 nutrient uptake for this study was another challenge because information on plant nutrient uptake is usually not available at a watershed scale. AnnAGNPS requires plant nutrient uptake, which is expressed as a ratio (weight of P/weight of dry matter at harvest), through the crop data section. AnnAGNPS converts the plant P uptake value into a daily value based on crop-growth stage. To gain information on plant nutrient uptake, an intensive literature search was conducted. Research on various cottons in Alabama and Louisiana showed that an average of 58 kg N/ha and 9.1 kg P/ha was removed when seed cotton was harvested under optimum fertilization condition (Bassett et al. 1970; Boquct and Breitenbeck 2000; Mullins and Burmester 1990). Based on the total dry matter production of cotton at harvest (Bassett et al. 1970; Boquct and Breitenbeck 2000; Mullins and Burmester 1990), cotton N uptake was set at 0.017, and cotton P uptake was set at 0.0023 (Boquct and Breitenbeck 2000; Mullins and Burmester 1990). Similarly, soybean N uptake was set at 0.092 and soybean P uptake at 0.0095 (Flannery 1986), whereas winter wheat N uptake was set at 0.022 and winter wheat P uptake at 0.0025 (Baethgen and Alley 1989). Taking these as base values, a sensitivity analysis is also needed to identify how plant P uptake impacts the simulation result. 9.5.3 SENSITIVITY ANALYSIS The purpose of a sensitivity analysis is to investigate input parameters, especially those that are difficult to measure or whose expected effect on model output is unclear (Lane and Ferreira 1980). The purpose of this sensitivity analysis was to identify parameters with effects that were greatest on P losses so that model users could focus their data collection on the more sensitive parameters. In a study of the Water Erosion Prediction Project (WEPP) model sensitivity, Nearing et al. (1990) used a single value to represent sensitivity of the output parameter over the entire range of the input parameter tested. The index described by Equation 9.24 (Nearing et al. 1990) was selected for sensitivity testing of the AnnAGNPS P losses. O2 − O1 O12 S= (9.24) I 2 − I1 I12 where I1 and I2 are the least and greatest values of input used, respectively, I12 is the average of I1 and I2, O1 and O2 are the output values in response to the two input values, and O12 is the average of O1 and O2. The parameter S represents the ratio of a relative normalized change in output to a normalized change in input. An index of 1 indicates a one-to-one relationship between the input and the output, such that a 1% relative change in the input leads to a 1% relative change in the output. A negative value indicates that input and output are inversely related. The greater the absolute value of the index, the greater the impact that an input parameter has on a particular output. Because it is dimen- sionless, S provides a basis for comparison among input variables. © 2007 by Taylor & Francis Group, LLC
  16. 230 Modeling Phosphorus in the Environment TABLE 9.1 Input Parameters Considered in the Sensitivity Analysis Values Input Parameters A B(Base Value) C P Mixing Code YES NO NA P application rate (kg/ha) NA 72.9 353.0 Initial soil P Organic P 50 500 NA content in Inorganic P 25 250 NA the top soil layer (mg/kg) Plant P uptake Cotton 0.0003 0.0023 0.0043 (ratio) Soybean 0.0075 0.0095 0.0115 Winter wheat 0.0005 0.0025 0.0045 = no value selected for sensitivity analysis for that situation. Note: NA As noted previously, soil nutrient content and plant nutrient uptake are difficult parameters to measure. Because the impact of fertilizer application on nutrient losses is a public concern, sensitivity analysis was performed for inorganic fertilizer appli- cation. The study investigated the sensitivity of the P losses from a watershed to changes in the following input parameters: (1) P mixing code, (2) P application rate, (3) initial P content in the top soil layer, and (4) plant P uptake. Phosphorus mixing code reflects how well the applied fertilizer is mixed within the depth of application. If the P mixing code is set to “YES,” applied fertilizer is well mixed within the depth of application. If the P mixing code is set to “NO,” the entire applied fertilizer remains on the soil surface. Values used for sensitivity analysis are listed in Table 9.1. For the P application rate, the actual amount of fertilizer applied was used as the base value. Base-value selections for initial soil P content and plant P uptake were discussed in Section 9.5.2. Each parameter varied individually within a range as reported in the literature (Baethgen and Alley 1989; Bassett et al. 1970; Boquct and Breitenbeck 2000; Mullins and Burmester 1990). The AnnAGNPS sensitivity analysis simulation was performed over a 4-year period. Annual average P loss was used as the output parameter for the sensitivity analysis. To evaluate the sensitivity of P mixing code (Table 9.2), four AnnAGNPS simulations were performed: two for P application rate B and two for P application rate C. Base values listed in Table 9.1 were used for initial soil P content and plant P uptake for all four simulations. No sensitivity index can be calculated for this analysis because no quantitative number is associated with P mixing code “YES” or “NO.” A percent error, which indicates relative changes of P losses from P mixing code “YES” to “NO,” was calculated (Table 9.2). First, the difference between P losses from P mixing codes “YES” and “NO” was calculated, and then the percent error was calculated as a ratio between the difference and P loss from P mixing code © 2007 by Taylor & Francis Group, LLC
  17. Phosphorus Modeling 231 TABLE 9.2 Sensitivity Analysis for the Effect of Mixing Code on P Losses Annual Average P Losses (kg/ha) P Application Rate B P Application Rate C P Mixing Code Attached P Dissolved P Attached P Dissolved P YES 1.683 15.610 1.707 18.713 NO 1.680 16.514 1.695 22.702 −0.055 −0.176 Percent error (ratio) 0.001 0.007 Notes: Application rate B = 72.9 kg/ha; application rate C = 353.0 kg/ha. Initial soil P content and plant P uptake remain as base values listed in Table 9.1. The percent error is calculated as the ratio of the difference between results from P mixing code “YES” and “NO” and results from P mixing code “NO.” TABLE 9.3 Sensitivity Indexes, S, of Selected Parameters on P Losses Sensitivity Index (S) for Sensitivity Index (S) for P Mixing Code “YES” P Mixing Code “NO” Input Parameters Attached P Dissolved P Attached P Dissolved P P application rate (kg/ha) 72.9 0.01 0.14 0.01 0.24 353.0 Initial soil Organic P 500 0.54 0.05 0.57 0.05 P content 50 in the top Inorganic P 250 0.19 0.94 0.22 0.84 soil layer 25 (mg/kg) Plant P Cotton 0.0003 –0.003 –0.03 –0.003 –0.03 uptake 0.0043 (ratio) Soybean 0.0075 –0.014 –0.12 –0.013 –0.11 0.0115 Winter 0.0005 –0.004 –0.03 –0.004 –0.03 wheat 0.0045 “NO.” The impact of P application rate on model predictions (Table 9.3) was performed using P application rates of 72.9 kg/ha and 353 kg/ha, and base values listed in Table 9.1 were used for initial soil P content and plant P uptake. The sensitivity index, S, was calculated using Equation 9.24 for sediment-attached P and dissolved P (Table 9.3). Similarly, the impact of initial soil P content and plant P uptake on model predictions was performed (Table 9.3). © 2007 by Taylor & Francis Group, LLC
  18. 232 Modeling Phosphorus in the Environment Sensitivity analysis results (Table 9.3) indicate that the most sensitive variables of those selected for analysis of P losses were initial soil P contents. This is consistent with previous studies (e.g., Fang et al. 2002; Pote et al. 1996, 1999; Sharpley 1995), which demonstrated that P losses to surface runoff were significantly correlated with soil P levels. Attached, or particulate, P loss is more sensitive to the initial soil organic P than soil inorganic P, but is not sensitive to fertilizer application rate and plant P uptake (Table 9.3). In contrast, dissolved P loss is very sensitive to the initial soil inorganic P, less sensitive to P application rate, and not sensitive to plant P uptake (Table 9.3). Because no sensitivity index can be calculated for the P mixing code, the sensitivity of P losses to P mixing code differs from the other parameters analyzed. It is similar, however, to P application rate in that the dissolved P is sensitive to the P mixing code whereas the attached P is not. The sensitivity of P losses to the P mixing code increases with the increase of P application rate as expected (Table 9.2). 9.5.4 MODEL CALIBRATION VALIDATION AND Since initial soil P content had the greatest impact on P losses among parameters tested for sensitivity (Table 9.3), the initial soil P content was adjusted to give good correspondence with the observed P losses. Initial soil P content selection for calibration was based on many studies of P summarized in Havlin et al. (1999). The first simulation was performed using 100 mg/kg for initial soil organic P content and 15 mg/kg for initial soil inorganic P content, which represents the lower level of soil P content. The second simulation was performed using 500 mg/kg for initial soil organic P content and 250 mg/kg for initial soil inorganic P content, which represents the average level of soil P content. The third simulation was performed using 1000 mg/kg for initial soil organic P content and 500 mg/kg for initial soil inorganic P content, which represents the high level of soil P content. The selection of initial soil P contents involved many trials and errors. The first 27 months were used to calibrate the model, and the last 22 months were used to validate the model. For plant P uptake, base values used for sensitivity analysis (Table 9.1) were chosen because these values were typical values under optimum fertilization. It is assumed that fertilizer applied in this study was the optimum value for crop uptake. Ann- AGNPS predicts P loss in dissolved and sediment-attached phases; thus, the pre- dicted total P loss was generated by summing dissolved and sediment-attached P losses (Table 9.4). The predicted and observed P losses listed in Table 9.4 do not include all P losses from the watershed. Although an attempt was made to collect samples for every storm event, some storm events were not sampled due to unfore- seen circumstances such as equipment malfunctions. Therefore, comparisons between model simulations and observations were made only when monitoring data were available. Linear regression and Nash-Sutcliffe coefficient of efficiency (Nash and Sutcliffe 1970) were calculated to evaluate the model’s performance (Table 9.4). The Nash-Sutcliffe coefficient of efficiency, E, ranges from −∞ to 1, with 1 indicating the model is a perfect prediction (Nash and Sutcliffe 1970). Calibration results showed that AnnAGNPS underpredicted total P loss and overpredicted dissolved P loss. Calibration demonstrated that increases in either © 2007 by Taylor & Francis Group, LLC
  19. Phosphorus Modeling TABLE 9.4 Monthly Observed Rainfall, Observed and Predicted Runoff, Sediment Loss, Dissolved P, and Total P Losses Runoff (mm) Sediment Loss (mg/ha) Dissolved P Loss (g/ha) Total P Loss (g/ha) Rainfalla (mm) Observed Predicted Observed Predicted Observed Predicted Observed Predicted Year Month 1996 October 63.8 4.8 25.6 0.02 0.15 23 65 23 164 November 122.4 27.4 49.5 0.07 0.09 67 196 115 254 December 127.5 70.6 71.2 0.13 0.18 59 131 211 261 1997 January 182.1 129.5 101.4 0.70 0.23 145 126 437 269 February(110)b 81.8 70.4 45.8 0.23 0.07 64 179 231 229 March(170.7)b 0.0 0.0 0.0 0.00 0.00 0 0 0 0 April 86.5 30.9 26.3 0.15 0.04 115 123 262 169 May 152.4 82.7 70.8 1.10 0.57 123 124 768 388 June 130.3 37.6 31.4 1.24 0.33 117 124 925 291 July 41.1 4.1 3.1 0.12 0.02 9 0 47 0 August(58)b 49.1 0.0 5.7 0.00 0.00 0 0 0 0 September(76)b 0.0 0.0 0.0 0.00 0.00 0 0 0 0 October 85.6 5.5 21.2 0.05 0.19 5 127 37 162 November 56.4 13.1 16.6 0.06 0.29 19 63 124 106 December 133.3 56.8 73.9 0.72 0.37 41 178 314 265 January(142)b 1998 106.6 59.3 69.6 0.58 0.51 39 121 378 174 February(98)b 90.0 36.5 35.3 0.47 0.22 41 55 389 69 March(95)b 88.7 37.7 18.9 0.18 0.08 9 119 86 216 April 130.8 72.6 48.9 0.46 0.43 101 177 468 305 May 111.5 84.6 64.3 0.81 2.08 13 63 748 524 June 31.0 12.3 7.8 0.29 0.09 27 61 144 86 233 (Continued) © 2007 by Taylor & Francis Group, LLC
  20. 234 TABLE 9.4 (CONTINUED) Monthly Observed Rainfall, Observed and Predicted Runoff, Sediment Loss, Dissolved P, and Total P Losses Runoff (mm) Sediment Loss (mg/ha) Dissolved P Loss (g/ha) Total P Loss (g/ha) Rainfalla (mm) Observed Predicted Observed Predicted Observed Predicted Observed Predicted Year Month July 166.1 53.6 48.8 0.23 0.42 142 242 255 463 August(29)b 0.0 0.0 0.0 0.00 0.00 0 0 0 0 September(74)b 0.0 0.0 0.0 0.00 0.00 0 0 0 0 October 27.2 0.0 0.0 0.00 0.00 0 0 0 0 November 141.2 39.9 50.8 0.11 0.70 262 129 394 573 December 205.2 155.0 134.4 0.51 1.51 454 258 1092 1331 Modeling Phosphorus in the Environment Total (C)c 1992 2661 7446 6299 = 0.52X + 62.3 R2 = 0.45 = 0.71X + 38.5 R2 = 0.61 Regression (C) c,d Y Y Nash-Sutcliffe 0.35 0.58 Coefficient (C)c 1999 January 224.3 214.8 147.3 1.68 1.89 187 250 1532 1601 February 50.0 7.2 8.1 0.04 0.04 6 56 46 73 March 120.4 58.1 45.9 0.24 0.22 45 122 288 270 April 110.0 65.4 47.5 0.19 0.30 313 122 569 314 May 73.7 6.5 7.0 0.10 0.12 56 51 132 70 June 29.8 0.0 2.2 0.00 0.00 0 0 0 0 July 7.1 0.0 0.1 0.05 0.01 0 0 0 0 August 0.0 0.0 0.0 0.00 0.00 0 0 0 0 September 40.5 0.0 3.6 0.00 0.00 0 0 0 0 © 2007 by Taylor & Francis Group, LLC
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