The Agricultural Policy Environmental extender (apex) Model: An Emerging Tool for Landscape and Watershed Environmental Analyses




НазваниеThe Agricultural Policy Environmental extender (apex) Model: An Emerging Tool for Landscape and Watershed Environmental Analyses
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The Agricultural Policy Environmental EXtender (APEX) Model: An Emerging Tool for Landscape
and Watershed Environmental Analyses



Philip W. Gassman, Jimmy R. Williams, Susan Wang, Ali Saleh, Edward Osei, Larry Hauck, César Izaurralde, and Joan Flowers


Technical Report 09-TR XX

March 2009


Center for Agricultural and Rural Development

Iowa State University

Ames, Iowa 50011-1070

www.card.iastate.edu


Philip Gassman is with the Center for Agricultural and Rural Development at Iowa State University. Jimmy Williams and Susan Wang are with the Blackland Research and Extension Center at Texas A&M University. Ali Saleh, Edward Osei, and Larry Hauck are with the Texas Institute for Applied Environmental Research at Tarleton State University, Stephenville, Texas. César Izarralde is with the Joint Global Change Research Institute at the University of Maryland.

Joan Flowers is with Carter & Burgess, Inc., Fort Worth, Texas.


This paper is available online on the CARD Web site: www.card.iastate.edu. Permission is granted to excerpt or quote this information with appropriate attribution to the authors.


Questions or comments about the contents of this paper should be directed to Philip Gassman, 560A Heady Hall, Iowa State University, Ames, IA 50011-1070; Ph: (515) 294-6313; Fax: (515) 294-6336; E-mail: pwgassma@iastate.edu.


This study was funded in part from support provided by the U.S. Department of Agriculture Natural Resources Conservation Service (project No. XX).


Iowa State University does not discriminate on the basis of race, color, age, religion, national origin, sexual orientation, gender identity, sex, marital status, disability, or status as a U.S. veteran. Inquiries can be directed to the Director of Equal Opportunity and Diversity, 3680 Beardshear Hall, (515) 294-7612.


The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and activities on the basis of race, color, national origin, gender, religion, age, disability, political beliefs, sexual orientation, and marital or family status. (Not all prohibited bases apply to all programs.) Persons with disabilities who require alternative means for communication of program information (Braille, large print, audiotape, etc.) should contact USDA’s TARGET Center at (202) 720-2600 (voice and TDD). To file a complaint of discrimination, write USDA, Director, Office of Civil Rights, Room 326-W, Whitten Building, 14th and Independence Avenue, SW, Washington, DC 20250-9410 or call (202) 720-5964 (voice and TDD). USDA is an equal opportunity provider and employer.


Abstract


Keywords:


Introduction


Extensive hydrologic and environmental model development has been carried out over the past four decades by the U.S. Department of Agriculture, Agricultural Research Service (USDA-ARS) and Texas A&M University, Texas AgriLIFE1 research units located in Temple, Texas, at the Grassland, Soil and Water Research Laboratory (GSWRL) and Blacklands Research and Extension Center (BREC), respectively (Williams et al., 2008). Early model investigation focused on unit hydrographs, flood routing estimation, sediment yield functions, and single event storm routing, followed by the development of weather generators, crop growth models, nutrient cycling routines, single event sediment and nutrient routing models, and the first daily time step continuous simulation water yield model. Many of the concepts developed in these earlier functions and models were incorporated into the Environmental Policy Impact Climate2 (EPIC) model (Williams et al., 1984; Williams, 1990, 1995; Gassman et al., 2005; Izaurralde et al., 2006) and the Simulator for Water Resources in Rural Basins (SWRRB) model (Williams et al., 1985; Arnold and Williams, 1987), which were designed to evaluate water quality and other agricultural environmental problems at the field scale and watershed scale, respectively. The SWRRB model was interfaced with the ROTO model (Arnold et al., 1995) and other model algorithms to construct the Soil and Water Assessment Tool (SWAT) model (Williams and Arnold, 1997; Arnold et al., 1998; Arnold and Fohrer, 2005; Gassman et al., 2007; Williams et al., 2008), which essentially replaced SWRRB for watershed analyses and extended the modeling capability to large river basin systems.

Both the EPIC and SWAT models have experienced continuous evolution since their inceptions and have emerged as key tools that are being used worldwide for analyzing a wide variety of environmental problems (Gassman et al., 2005; Gassman et al., 2007). Examples of EPIC applications include plot- or field-level assessments of sediment, nutrient, and/or pesticide loss as a function of different cropping or management systems (Jackson et al., 1994; Chung et al., 2002; Lu et al., 2003; Wang et al., 2006b), field-level crop yield, nitrogen cycling, or soil carbon sequestration evaluations (Williams et al., 1989; Cabelguenne et al., 1990; Cavero et al., 1999; Wang et al., 2005; Izaurralde et al., 2006), regional-level assessments of nitrogen leaching, soil carbon sequestration, or other environmental indicators (Wu and Babcock, 1999; Cepuder and Shukla, 2002; Gaiser et al., 2008) including interfaces with economic analyses (Feng et al. 2005; 2007), and global assessments of crop yield as a function of climate change, land use change, or water management (Tan and Shibaski, 2003; Wu et al., 2007; Liu et al., 2007). SWAT applications range from hydrologic and/or pollutant loss validation studies (Saleh et al., 2000; Jha et al., 2007; Reungsang et al., 2007; Green and van Griensven, 2008; Stehr et al., 2008), to hydrologic assessments of climate change, reservoir, wetland, or tile drainage effects, or land use change across a variety of watershed scales (Jha et al., 2006; Gosain et al., 2006; Green et al., 2006; Wu and Johnston, 2007; Jones et al., 2008; Wang et al., 2008e; Cao et al., 2008), to best management practice (BMP), land use, and other scenario analyses on pollutant losses (Nelson et al., 2005; Secchi et al., 2007; Volk et al., 2008; Ghebremichael et al., 2008; Parajuli et al., 2008), to hydrologic balance, climate change or other analyses of huge river basins or water resource systems at the national, subcontinent, or entire continent scale (Arnold et al., 1999; Thomson et al., 2003; Schuol et al., 2008a,b). A comprehensive review of dozens of SWAT studies performed worldwide is provided by Gassman et al., 2007.

Significant gaps in the ability to simulate key landscape processes at the farm or small watershed scale persisted, despite the versatility of these two models. This weakness was acutely revealed at the onset of the Livestock and the Environment: A National Pilot Project (NPP), which was commissioned in the early 1990s to address water quality and other environmental problems associated with intensive livestock production. A key objective of the NPP was to evaluate a wide range of alternative manure management scenarios that included relatively complex combinations of farm-level landscapes, cropping systems, and/or management practices. Thus, the NPP served as a catalyst for the development of the initial versions of the Agricultural Policy Environmental EXtender (APEX) model (Williams et al., 1995; Williams, 2002; Williams and Izaurralde, 2006; Williams et al., 2006; Williams et al., 2008), which bridged the gap that existed between the EPIC and SWAT models.

The APEX model is a flexible and dynamic tool that is capable of simulating management and land use impacts for whole farms and small watersheds. APEX is essentially a multi-field version of the predecessor EPIC model and can be executed for single fields similar to EPIC as well as for a whole farm or watershed that is subdivided based on fields, soil types, landscape positions, or subwatersheds. APEX functions on a daily time step, can perform long-term continuous simulations, and can be used for simulating the impacts of different nutrient management practices, tillage operations, conservation practices, alternative cropping systems, and other management practices on surface runoff and losses of sediment, nutrient, and other pollutant indicators. The model can also be configured for novel land management strategies such as filter strip impacts on pollutant losses from upslope cropfields, intensive rotational grazing scenarios depicting movement of cows between paddocks, vegetated grassed waterways in combination with filter strip impacts, and land application of manure removal from livestock feedlots or waste storage ponds. Routing of water and pollutants can be simulated between subareas and through channel systems in the model. According to Srivastava et al. (2007), APEX is one of the few existing models that is capable of simulating flow and pollutant transport routing at the field scale.

The objective of this study is four-fold: (1) briefly describe the major components of APEX and differentiate between existing important versions; (2) provide a review of APEX applications reported in the peer-reviewed literature and other sources, including validation assessments versus measured data; (3) describe GIS and other interface tools that have been developed to facilitate APEX applications for watershed- and regional-scale assessments as well as nested applications within a SWAT watershed study; and (4) discuss future research and development needs for the model.


APEX MODEL DESCRIPTION


Williams et al. (1995) provided the first qualitative description of the APEX, which included a description of the major components of the model, including the manure management component. Expanded qualitative descriptions of the model are reported by Williams (2002) and Williams et al. (2006), the latter of which provides overviews of the manure erosion and routing components, including some mathematical description. Williams and Izaurralde (2006) provide an exhaustive qualitative description of the model coupled with mathematical theory for several of the components. Complete theoretical descriptions of APEX were initially compiled by Williams et al. (2000) and Williams and Izaurralde (2005); Williams et al. (2008) provide an updated, in-depth theoretical manual for the latest APEX model (version 0604).

A brief qualitative overview of key APEX components is provided here, based in part on the discussion provided in Williams et al. (2006). The above referenced documents should be consulted for more detailed descriptions of the different model components. Previous documentation for the EPIC model also provides relevant background information for APEX, which is cited in Gassman et al., 2005.


Overview of APEX

The APEX code is written in FORTRAN and can be executed on a PC (for most operating systems) and also on a UNIX platform. The model consists of 12 major components: climate, hydrology, crop growth, pesticide fate, nutrient cycling, erosion-sedimentation, carbon cycling, management practices, soil temperature, plant environment control, economic budgets, and subarea/routing. Management capabilities include sprinkler drip or furrow irrigation, drainage, furrow diking, buffer strips, terraces, waterways, fertilization, manure management, lagoons, reservoirs, crop rotation and selection, cover crops, biomass removal, pesticide application, grazing, and tillage. Simulation of liquid waste applications from concentrated animal feeding operation (CAFO) waste storage ponds or lagoons is a key component of the model. Stockpiling and subsequent land application of solid manure in feedlot or other animal feeding areas can also be simulated in APEX. Groundwater and reservoir components have been incorporated in APEX in addition to the routing algorithms. The routing mechanisms provide for evaluation of interactions between subareas involving surface run-on, return flow, sediment deposition and degradation, nutrient transport, and groundwater flow. Water quality in terms of soluble and organic N and P and pesticide losses may be estimated for each subarea and at the watershed outlet.


Climate Inputs

Precipitation, maximum and minimum temperature, and solar radiation are the daily climate inputs required for driving APEX. Wind speed and relative humidity are also required for some evapotranspiration options as described below, and wind speed is further required if wind erosion is simulated. Climate data can be entered from recorded measurements, generated internally in the model, or provided in several different combinations of both measured and generated data. Tabulated monthly weather statistics are required for generating weather in APEX and are also required for other stochastic processes, and thus must be inputted for every APEX simulation.

Precipitation is generated in the model based on a first-order Markov Chain model developed by Nicks (1974), which is also used in the CLIGEN weather generator (Meyer et al., 2008). Precipitation can also be generated spatially for watershed applications covering larger areas and/or encompassing regions with steep rainfall gradients. Air temperature and solar radiation is generated in the model using a multivariate generation approach described by Richardson (1981). Wind generation in APEX is based on the Wind Erosion Continuous Simulation (WECS) model (Potter et al., 1998), which requires estimation of wind speed distribution within each day and the dominant wind direction. Average relative humidity is estimated each day from the tabulated average monthly value using a triangular distribution.


Hydrologic Balance

The hydrologic balance component of APEX encompasses all of the key processes that occur in the hydrologic cycle. Initially, incoming precipitation, snowmelt water, or irrigation input is partitioned between surface runoff and infiltration. Infiltrated water can be stored in the soil profile, percolate vertically to groundwater, be lost via evapotranspiration, or routed laterally in subsurface or tile drainage flow. Return flow to stream channels from groundwater or lateral subsurface flow is accounted for. Fluctuations in water table depth can also be simulated to account for offsite water effects; however, there is no direct linkage between the water table calculations and other hydrologic processes simulated in the model.

Surface runoff volume can be estimated with two different methods in APEX: a modification of the Soil Conservation Service (SCS) runoff curve number (RCN) technique (USDA-NRCS, 2004) described by Williams (1995), and the Green and Ampt infiltration equation (Green and Ampt, 1911). Two additional options are provided regarding the estimation of the RCN retention parameter, which are based on either the traditional soil moisture approach or an alternative algorithm computed as a function of evapotranspiration. The alternative retention parameter option is described by Kannan et al. (2007) and Yin et al. (2008) and can result in more accurate runoff estimations for some soil and land cover conditions. Daily rainfall data is used with the RCN technique while subdaily rainfall is used in the Green and Ampt approach, which is computed by distributing daily rainfall exponentially with stochastically generated parameters.

The peak runoff rate is also estimated in APEX for each storm event, which is used in calculating erosion loss as described below. The peak runoff rate can be estimated using the modified Rational Formula (Williams, 1995) or the USDA-SCS TR-55 method (USDA-SCS, 1986) as a function of rainfall intensity and other factors. A peak runoff rate is also estimated for snowmelt events, assuming a uniform distribution of rainfall over a day and no rainfall energy.

Subsurface flow is calculated as a function of both vertical and horizontal subsurface flows. Simultaneous computation of the vertical and horizontal subsurface flows is performed in the model, using storage routing and pipe flow equations. Vertical percolation of infiltrated water is routed through successive soil layers using a storage routing approach as a function of key soil parameters including the field capacity (maximum soil water holding capacity), saturated conductivity, and porosity. Flow from an upper soil layer to the next soil layer occurs when the soil water content in the first soil layer exceeds field capacity and continues from that layer until the soil water content reaches field capacity again. This routing process continues until the flow reaches groundwater storage, which can lose water because of deep percolation from the overall system and also return flow to the stream channel; the return flow is routed to the channel flow in the subarea in which the return flow was calculated. Upward water movement from a soil layer can also occur when the soil water content of the lower layer exceeds field capacity while the upper layer soil water content is less than field capacity. In frozen soils, water can percolate into a frozen layer but cannot percolate into a lower layer.

Horizontal flow is partitioned into lateral and quick return flow. Lateral subsurface flow enters the subarea immediately downstream and is added to that subarea’s soil water storage. Quick return flow is added to the channel flow from the subarea. Tile drainage flow can also be simulated, which is calculated as a modification of the natural lateral subsurface flow. The tile drainage calculations are performed as a function of tile drainage depth and the time (in days) required for the drainage system to reduce crop stress due to excess water in the soil profile.

Five different options are provided in APEX for estimating potential evaporation: Hargreaves (Hargreaves and Samani, 1985), Penman (1948), Priestley-Taylor (1972), Penman-Monteith (Monteith, 1965), and Baier-Robertson (1965). The Penman and Penman-Monteith methods are the most data intensive, requiring solar radiation, air temperature, wind speed, and relative humidity as input. The Priestly-Taylor method requires solar radiation and air temperature as input while the Hargreaves and Baier-Robertson methods require only air temperature. The Baier-Robertson method was developed in Canada and can provide more accurate potential evaporation estimates for colder climate conditions. The model computes evaporation from soils and plants separately, as described by Ritchie (1972).


Water and Wind Erosion


Water-induced erosion is calculated in APEX in response to rainfall, snowmelt, and/or irrigation runoff events. Seven different equations are provided in APEX for calculating water erosion: the Universal Soil Loss Equation (USLE) method (Wischmeier and Smith, 1978), the Onstad-Foster modification of the USLE (Onstad and Foster, 1975), the Modified USLE (MUSLE) method (Williams, 1975), two MUSLE variants (Williams, 1995), a MUSLE approach that uses input coefficients, the Revised USLE (RUSLE) method (Renard, et al. 1997), and RUSLE2. Multiple equations can be activated during a simulation, but only one interacts with other APEX components, as specified by the user. The seven equations are similar except for their energy components. The USLE and RUSLE depend strictly upon rainfall as an indicator of erosive energy while the MUSLE and its variations use only runoff variables to simulate erosion and sediment yield. The runoff variables result in increased prediction accuracy, eliminate the need for a delivery ratio (used in the USLE to estimate sediment yield), and allow the various MUSLE equation variants to predict single storm estimates of sediment yields.

The original wind erosion model used in EPIC was the WEQ (Williams, 1995), which has since been replaced by the Wind Erosion Continuous Simulator (WECS) approach (Potter et al., 1998). The potential wind erosion is estimated for a smooth, bare soil each day by integrating the wind erosion equation over the day as a function of the inputted wind speed distribution. The actual erosion is computed based on adjustments to the potential erosion by factoring in the effects of soil properties, surface roughness, vegetation cover, and distance across the field in the wind direction.


Carbon Cycling Routine

The latest versions of APEX incorporate enhanced carbon and nitrogen cycling algorithms, initially developed by Izaurralde et al. (2006) for EPIC, which are based on concepts used in the Century model (Parton et al., 1987, 1993). These routines estimate soil carbon sequestration as a function of climatic conditions, soil properties, and management practices and simulate storage of carbon and nitrogen compounds in either structural or metabolic litter, biomass, or slow and passive soil humus pools. Direct interaction is simulated between these pools and the EPIC soil moisture, temperature, erosion, tillage, soil density, leaching, and translocation functions. Other features of the carbon cycling approach in APEX include the following: (1) organic materials’ movement from surface litter to subsurface layers are estimated by the leaching equations currently in APEX; (2) temperature and water controls affecting transformation rates are calculated with equations currently in APEX; (3) the surface soil layer in APEX has a slow but no passive humus compartment (unlike the Century model which has both); and (4) the lignin concentration in APEX is modeled as a sigmoidal function of plant age.


Nitrogen Cycling and Losses

The complete nitrogen (N) cycle is simulated in APEX, including atmospheric N inputs; fertilizer and manure N applications; crop N uptake; denitrification; mineralization; immobilization; nitrification; ammonia volatilization; organic N transport on sediment; and nitrate-nitrogen (NO3-N) losses in leaching, surface runoff, lateral subsurface flow, and tile flow.

As one of the microbial processes, denitrification is a function of temperature and water content (Williams, 1995). Anaerobic conditions are required and a carbon source must be present for denitrification to occur. Nitrification, the conversion of ammonia N to NO3-N, is estimated using a combination of the methods of Reddy et al. (1979) and Godwin et al. (1984). The approach is based on the first-order kinetic rate equation of Reddy et al. (1979). The equation combines nitrification and volatilization regulators. The nitrification regulator is a function of temperature, soil water content, and soil pH.

Simulation of atmospheric emissions of N gases from the soil profile in APEX include N2 and N2O, as products of denitrification, and ammonia volatilization. The N2 and N2O emissions are simulated in APEX by using a common rational of adjusting a maximum, empirically determined emission rate using factors that control the total denitrification rate. The total denitrification rate is then partitioned into N2 and N2O fluxes. Volatilization, the loss of ammonia to the atmosphere, is estimated simultaneously with nitrification. Volatilization of surface-applied ammonia is estimated as a function of temperature and wind speed (Williams, 1995). Depth of ammonia within the soil, cation exchange capacity of the soil, and soil temperature are used in estimating below-surface volatilization.

A loading function developed by McElroy et al. (1976) and modified by Williams and Hann (1978) for application to individual runoff events is used to estimate organic N loss. The loading function considers sediment yield, organic N loss in the soil surface, and an enrichment ratio. The amount of NO3-N lost when water flows through a layer is estimated by considering the change in loss (Williams, 1995). NO3-N loss in a soil layer decreases exponentially as a function of flow volume. The average loss during a day is obtained by integrating the exponential function with respect to flow. Amounts of NO3-N contained in runoff, lateral flow, and percolation are estimated as products of the volume of water and the average loss.


Phosphorus Cycling and Losses

The APEX approach is based on the concept of partitioning pesticides into the solution and sediment phases (Knisel, 1980). Because P is mostly associated with the sediment phase, the soluble P runoff equation is a linear function of soluble P loss in the top soil layer, runoff volume, and a linear adsorption isotherm. Sediment transport of P is simulated with a loading function as described in organic N transport. The P loading function considers sediment yield, organic P loss in the top soil layer, and the enrichment ratio. The P mineralization model developed by Jones et al. (1984) is a modification (Williams, 1995) of the PAPRAN mineralization model (Seligman and van Keulen, 1981). Mineralization from the fresh organic P pool is estimated as the product of the mineralization rate constant and the fresh organic P content. Mineralization of organic P associated with humus is estimated for each soil layer as a function of soil water content, temperature, and bulk density. The P immobilization model was also developed by Jones et al. (1984). The daily amount of immobilization is computed by subtracting the amount of P contained in the crop residue from the amount assimilated by the microorganisms.


Livestock Grazing

All subareas are identified by an ownership number, and each owner may have livestock and poultry. The owner may have up to 10 herds or groups of animals. The identifying attributes of each herd are forage intake rate in kg head-1 d-1, grazing efficiency (accounts for waste by trampling, etc.), manure production rate in kg head-1 d-1, urine production in l head-1 d-1, and C and soluble and organic N and P fractions in the manure. Only one herd may occupy a subarea at any time. All livestock rotations among subareas are performed automatically by APEX within user constraints. There is a provision for leading and trailing rotations. For example, stocker steers could be rotated ahead of the cow-calf herd so that they always get the best quality forage. The complex grazing systems are created by indicating the number of head in each herd, the herd identification numbers (in order of grazing priority) eligible to graze each subarea, and a lower grazing limit (above ground biomass in t ha-1) for each herd on each subarea. The animals may be confined to a feeding area totally or for a fraction of each day. Grazing may occur throughout the year or may be allowed only at certain times. Grazing stops automatically when the subarea lower limit is reached. If the owner has other eligible grazing subareas, the animals move automatically to the one with the most above-ground biomass. If the owner has no more eligible grazing areas, the animals remain on the overgrazed area, and supplemental feeding is assumed. This rotational grazing process continues throughout the simulation. The grazing system provides flexibility for such conditions as confined or partially confined area feeding, intensive rotational grazing, and cropland grazing after harvest.

Manure Management

Manure may be applied in solid or liquid form. Confined feeding areas may contain a lagoon to catch runoff from the feeding area plus wash water that is used in the barn. The lagoon is designed automatically by the model considering normal and maximum volumes. Effluent from the lagoon is applied automatically to a field designated for liquid manure application. The liquid manure application rules are as follows: (1) pumping begins when the lagoon volume exceeds 0.75 of the difference between maximum and normal lagoon volumes; (2) the pumping rate is set to reduce the lagoon volume from maximum to normal in a user-supplied number of days; (3) pumping can also be triggered by a user-supplied date—usually before winter or a high rainfall season. Solid manure is scraped from the feeding area automatically at a user input interval in days and stockpiled for automatic application to designated fields. An owner may have any number of solid manure application fields. When an application is triggered (the stockpile is adequate to supply the specified rate), manure is applied to the field with the lowest soluble P concentration in the top 50 mm of soil. A variety of livestock, including cattle, swine, and poultry, may be considered because manure production in kg head-1 d-1 and its ingredients (mineral and organic N and P) are inputs. APEX simulates runoff, soil erosion, and manure erosion. Routing mechanisms simulate soluble nutrient transport with water, organic nutrient transport by sediment, and manure transport by water.


Manure Erosion

Nutrient losses from feedlots and manure application fields can be estimated in APEX using a manure erosion equation based on the previously described MUST equation, which provides direct estimates of organic nutrient and carbon losses. The simulated erosion can consist of essentially just manure to a combination of manure and soil, depending on the extent of manure coverage across a feedlot or field. Since manure is considered residue, a heavy manure cover in a feedlot may completely eliminate soil erosion because of the “residue effect” of the manure; however, this condition could potentially result in extreme manure erosion. Analogous results can occur for fields with well-established stands of grass or similar vegetative cover.


APEX Routing Component

Current versions of APEX now offer two options for routing water through channels and flood plains: a daily time step average flow method, and a short time interval complete flood routing method. If the primary purpose is to simulate long-term water, sediment, nutrient, and pesticide yields from whole farms and small watersheds, the daily time step method should produce realistic estimates and is computationally efficient. However, the complete flood routing provides estimates of actual stream flow and potentially increases accuracy in estimating pollutant transport, especially when simulating larger watersheds.

The average flow rate for a runoff event is estimated as a function of runoff volume, watershed area, rainfall duration, and time of concentration for the daily time step average flow method. The channel capacity is estimated using Manning’s equation assuming a trapezoidal shape. If the daily flow rate is less than channel capacity flow is contained in the channel and the flow velocity is calculated using Newton’s method for solving nonlinear equations. The solution involves adjusting flow depth to give the correct flow rate. Then channel flow velocity is computed by dividing rate by cross-sectional area. If the channel capacity is exceeded, the excess flow occurs in the floodplain. Flow depth is calculated using Manning’s equation. Flow velocity is computed by dividing rate by area. Travel time through the reach floodplain is length divided by velocity. The inflow volume is reduced by floodplain infiltration.

The Variable Storage Coefficient (VSC) flood routing method (Williams, 1975) is used for simulating hydrographs with short (typically 0.1 to 1.0 h) time steps for the more complete flood routing approach. Runoff hydrographs from subareas are simulated and routed downstream to the watershed outlet. This complete flood routing approach simulates dynamic stream flow whereas the daily time step method can only estimate daily water yield (daily simulated runoff from all subareas arrives at the watershed outlet at the end of the day). This is an important feature for watersheds with times of flow concentration of 0.5 d or more. It is also important in estimating flood stages and durations and pollutant transport capacities. Storm event rainfall-time distributions are derived from daily rainfall. Rainfall excess is then estimated and applied to the accumulated rainfall distributions in user specified time steps. Runoff hydrographs are simulated with a variation of the VSC method called the storage depletion technique. The watershed storage volume is computed at each time interval by adding the simulated rainfall excess for that time interval to the existing storage volume.

Sediment is routed through the channel and floodplain separately. The same sediment routing equations are used for daily time step routing and for the VSC method. If daily time step routing is used, the velocities and flow rates are the averages for the day and the volume is the total for the day. If the VSC method is used, average velocity, flow rate, volume, and sediment transport are calculated for each time interval. Thus, the VSC produces time distributions of sediment concentration and transport (sediment graphs). The sediment routing equation is a variation of Bagnold’s sediment transport equation (Bagnold, 1977); the new equation estimates the transport concentration capacity as a function of velocity.

The organic forms of N and P are transported by sediment and are routed using an enrichment ratio approach. The enrichment ratio is estimated as the ratio of the mean sediment particle size distribution of the outflow divided by that of the inflow. Mineral forms of N and P are considered conservative and thus maintain a constant loss as they flow through a reach. Mineral nutrient losses occur only if flow is lost within the reach. The pesticide routing approach is the same as described for nutrients. The adsorbed pesticide phase is transported with sediment using the enrichment ratio and the soluble phase is transported with flow in a conservative manner.


The Reservoir Component


A reservoir may be placed at the outlet of any subarea, and inflow is derived from the subarea plus all other contributing subareas. Reservoirs are designed with principal and emergency spillways to accommodate a variety of structures. Typically the principal spillway elevation is set at the top of the sediment pool. The amount of flood storage is determined by the storage volume between the principal and emergency spillways. Sediment and attached nutrients and pesticides are deposited in reservoirs, but soluble materials are considered conservative.
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