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HomeMy WebLinkAboutGround Water Contamination Model for Diagnosing 1985,SEP 3 0 A PRAGMATIC MODEL for DIAGNOSING and FORECASTING GROUND WATER CONTAMINATION T~MMO S. STEENHUtS*, MARJOLEIN VAN DER MAREL*, and STEVEN PACENKA** Cornell University Department of Agricultural Engineering* and Center for Environmental Research** Ithaca, NY 14853 Abstract A mathematical management model for tracking the movement and fate of a soluble chemical in the unsaturated and saturated zones is described and tested against field and'laboratory data. The model is named MOUSE which is the acronym for "Method Of Underground Solute Evaluation". MOUSE's soft- ware runs on an IBM PC microcomputer. MOUSE divides the surface and subsurface into three parts consisting of the atmosphere, the unsaturated zone and the aquifer. The model simu- lates these three parts using four linked submodels each of which can also be used in a stand alone mode. They perform the following functions: generation of synthetic climate patterns; calculations of moisture content and fluxes in the unsaturated zone; simulation of degradation and movement of the solute In the unsaturated zone and finally the movement of water and the solute and attent~ation of pesticides in the aquifer. MOUSE is a management model with intended use as a training tool for proiessionals and sit,dents. It will.give them a better understanding of ground water contaminattun. Another use envisioned is screening pesticides under different environmental conditions. The predictions of the model are compared against recharge measurements and pesticide concentrations in the ,msaturated and saturated zones. In all cases model predictions are in reasonable agreement with the observed values. Proceedings of Practical Application of Ground Water Models August 1984. National Water Well Association Introduction Ground water provides half of the U.S. population with their drinking water. About 95 percent of the nation's rural population are similarly dependent (U.S. Water Resources Council, 1978). Despite this substantial reliance, we collectively know little about the relationships between our act£vtties on the land surface and the quantity and quality of the water which recharges this vital resource. Relatively recent national concern about the negative impacts some land uae activities have on ground water has spurred a great deal of governmental activity relative to this resource. Numerous communities are seeking ways to safeguard (or restore, in too amny cases) their local ground water supplies. At the Federal level, the U.S. EPA has proposed to reallocate existing funds to give ground water a much mere significant place in their operations (U.S. EPA, 1984). What motivates this activity is a recognition of the number of unsolved contamination cases coupled with an acknowledgment of our historical state of ignorance about ground water in general. One major contribution to this new effort to understand aquifers for management has been research into mathematical models which represent the behavior of contaminants introduced on and into the land. Such models are essential in understanding such normally invisible, expensive-to-observe, and significantly complex systems. This paper describes one such mathematical model, termed MOUSE, which is the acronym for "Method Of Underground Solute Evaluation." MOUSE packages mathematical algorithms describing the soil-water- chemical system into a highly interactive computer program. The model represents the complete subsurface domain, tracking the movement and behavior of a chemical from its introduction at the land surface until its possible eventual reappearance in a surface water body. The computer program is designed to teach its user about key factors affecting the fate of the introduced chemical, by allowing rapid changes of parameter values and by portraying the simulated behavior of the system dynamically using a graphical display. The two main intended uses of this program are: to train planners, public officials and students (as future decision makers) about general aspects of the environmental processes which affect the transport and fate of a chemical introduced at the land surface; and to help perform preliminary evaluations ("screening") of the potential for ground water contamination by specific chemicals used under different soil-climate-management regimes. Mathematical Models for Water and Contaminant Accounting MOUSE is part of a rich context. There are many published models which trace soluble chemicals through the vadose zone to ground water. There are also numerous models available which route chemicals through the saturated zone of an aquifer. MOUSE does not break new ground in modeling theory; rather, it packages selected, proven techniques into a comprehen- sive and usable framework. Types of Models Addiscott and Wagenet's (1984) classification scheme divides the continuum of models in several ways: - deterministic versus stochastic; - mechanistic versus functional; and - rate versus capacity. Deterministic models presume that the changes of state in the system are uniquely governed by one given set of input parameters. Stochastic models presuppose that the path followed will be uncertain and incorporate this uncertainty explicitly in their structures. Much of the impetus for development of stochastic models arises from the extreme soil variability measured in the field (e.g., Bigger and Nielsen, 1976). Most deterministic models cannot account for small-scale spatial variability. In theory, stochastic approaches can represent this variation, but they remain severely limited by the lack of quantitative knowledge about spatial variability in specific field soils. Stochastic models and patterns of field variability are the foci of a major wave of current research. Mechanistic models consist of a network of equations which represent fundamental physical, chemical, and biological processes in rich detail. Conceptually elegant, these models can be difficult to apply to an arbitrary field situation and also may require rather substantial computer expertise and resources to operate (Wagenet and Rao, 1984). Functional modeling approaches sacrifice this detail for sufficiency of results. Simplifying assumptions in functional models limit their resolution and precision to gain ease and economy of use. These models are diverse because they embrace a variety of simplifying assumptions. Rate models treat fluxes and changes of storage of water and chemicals using differential equations solved over short time intervals. In some cases they can employ analytical solutions. Capacity models use difference equations to define changes in amounts of solute and water content over finite intervals. These typically use time intervals of a day or longer, whereas some numerical rate models must resort to time steps on the order of minutes to follow the transient flow of infiltrating water. By this classification system, MOUSE is a deterministic, functional, rate model. Like all deterministic models, it allows the consequences of variability in parameters to be assessed by way of repeated conditional simulations. MOUSE fits into the functional category since its governing differential equations of water and solute flux are simplified to allow closed-form solutions. As a result the model can use large time steps, thereby reducing the execution time significantly. The simplifications introduced probably do not degrade computational accuracy beyond the limits imposed by uncertainties in the field data with which it will be applied and tested. The choice of model characteristics depends heavily upon the intended uses. Two broad types of usage include research and agricultural and envlronn~ntal management. Research models aim at advancing the state of knowledge about fundamental environmental processes, and thus tend to fit into the mechanistic and rate categories. Management models sacrifice detail for practicality of regular application and use. Simpler and more functional models can be used in a ~rlder variety of situations, especially where the less detailed results are sufficient to satisfy the manager's needs. MOUSE's aims and simplifications make it a menagement model. Design Criteria Management models used by environmental planners ideally have the following characteristics (modified after Haith et al., 1984): 1. Input data are generally available from standard published 2. The amount of computer expertise required is minimal. 3. Computational needs are sufficiently modest to permit long-term simulations. 4. The algorlthn~ are based on sound physical principles and can be used with a minimum of locally-~easured field data. These criteria also apply in training settings where the aim is to transfer appropriate conceptual models in users' minds. MOUSE's use of dynamic graphical displays of simulated solute and water processes and Its interactive control structure both assist in transferring such informa- tion. These features work together to permit repeated trial runs with different asau~ptions and quick recognition of their consequences. Screening is facilitated by a general structure which permits widely varying chemical, soil, and climate characteristics to be represented using different parameter values. Most ~arameters needed may easily be estimated from standard sources, such as SCS soil survey reports and National Weather Service climatic records. By changing parameter values, conditions in most of the continental U.S. can be represented. The Overall Structure of MOUSE For simplicity of construction and operation, the total model is broken up into four loosely-connected submedels, each of which can also be used by itself. A submodel obtains its parameters from one disk file, obtains [ts boundary inflows from a different file created by its predeces- sor submodel (if any), and saves its own results into a third file for use by the next module in the chain. Each submodel also contains a screen- oriented parameter editor and displays its dynamic results in graphical form. The modular structure permits eubstitution of submodels using different algorithms into any slot, and also allows mmasured data for boundary inflows to be inserted when testing a submodel. The submodels are: - Synthetic Climate Generator Vadose Water Balancer Vadose Solute Transporter Aquifer Water and Solute Transporter The Synthetic Climate Generator generates daily precipitation, air temperature, and soil temperature at a 10 cm depth. The Vadose Water Balancer simulates water flows and storages within the unsaturated zone, calculating evapotranspiration, moisture movement between vertical soil strata, and recharge to the ground water. The Vadose Solute Transporter superimposes solute movement on the calculated water fluxes, adjusted for adsorption and desorption of the solute on the porous medium. Other processes it represents include chemical diffusion and dispersion and possible degradation due to biochemical action. The end reeult of this submodel is a time series of solute addition to the aquifer. The Aquifer Water and Solute Transporter simulates the movement and the degradation of the solute in a vertical, two-dimenelonal cross-section of an aquifer. Water movement is assumed to obey steady state Dupuit equations. Chemical transport is based on water movement, linear adsorption/dasorption, and first order degradation. The following sections of this paper provide overviews of the four submodels. In the final section the realism of each euhmodel is assessed relative to the degree needed for the intended uses. Synthetic Climate Generator The synthetic climate generator calculates daily precipitation, and air temperature. The cumbersome task of entering daily climate data can be avoided with this submodel. The model uses monthly climate statistics from historical records to produce daily weather patter'ne with statistical properties similar to the historical pattern. The input data needed are the average total precipitation amounts by month; the average number of days per month with more than 0.25 mm of precipitation; the average monthly temperatures for January and July; and the number of years to be simulated. The climate data are eaeily obtainable in summarized form from records compiled by the National Weather Service. The output consists of a plot and file written to disk consisting of daily precipitation, daily air temperature and soil temperature. The plot is generated on the monitor during the simulation (Figure 1). The disk file is used by the Vadose Water Balancer. SIMULATED CLIMATE: Bridgehampton, N. Y. oc 10- 0- -10- C M 5- O J F M A M J J A S O N D Air,Soil Temp. ,~:~ ! ..~.. ..:"": ' '"': :~.- ..;. Precipitation Figure 1: Screen Display of the Synthetic Climate Generator. Methods Used for Simulation The simulation of the daily precipitation and temperature is based on the work of Pickertng (Picketing, 1982; Haith et al. [984). This model was chosen because of its simplicity and availability. Daily precipitation is simulated using a Markov chain method which is driven by conditional probabilities of precipitation given that a previous day had rain or not based on historical records. This determines whether any precipitation at all falls on a day. The equations for the probability of precipitation are: pdd - 0.1718 + 0.8642*WD/MD (t) pdw = WD*(I - pdd) (2) where pdd is the probability of precipitation after a dry day, pdw is the probability of precipitation after a previous wet day, W~ is the average number of wet days in the month, and ~ is the total number of days in the month. (WD can be taken from historical statistics). The coefficients in the first equation are from Pickering's work. The occurrence of precipitation uses these conditional probabilities and a random number from a uniform distribution between 0 and 1. The amount of precipitation falling on a "wet" day is also random, based on an exponential distribution: P = 0.0127 + I (3) (PM - 0.0127)*1n(1 - U) where P is the amount of precipitation (cm), PM is the mean amount of precipitation on a wet day in the current month (from historical statis- tics), and U is a random number from a uniform distribution between 0 and 1. The 0.0127 is half of the minimal amount of precipitation on a wet day. Daily air temperatures are simulated by assuming that the average temperature follows an annual sinusoidal pattern with random perturbations: t[d] = u[d] + cl*(c[d-l] - u[d]) + /' ( I - cl*cl )*s[d]*V (4) cl is a constant estimated by Picketing (1982) as 0.65; d is the Julian day; t[d] is the day's air temperature; and V is a random number between 0 and I from a normal distribution; u[d] is the average temperature for this day, computed by u[d] Ujuly - UJan * { 1 + sin( (d - 100_,) ~) } + u]an (5) 2 180 which fits a sine wave between the average ~nthly temperatures of July and January (Ujul¥ and Ujan respectively), s[d] is the standard deviation of ~he day's temperature, based on s[d] = 5.72 - O.122*u[d] (6) Soil temperature at 10 cm depth is computed as the average of the air temperature of the previous 20 days. The Vadose Water Balancer The Vadose Water Balancer simulates the movement and retention of water in the unsaturated zone above the gronnd water table for a given crop, soil and climate. The submodel accounts for snowfall, snowmalt. rainfall, runoff, infiltration, evaporation, transpiration, downward movement (percolation and recharge) and changes in snow and liquid water storage. The input consists of daily precipitation and temperature, which is obtained from a disk file created by the Synthetic Climate Generator. Other climatic input consists of potential evaporation. The user may supply observed data. The evapotranspiration may also be simulated by supplying the program with the average monthly potential evapotranspiration for January and July. Soil hydrologic parameters are also required. For each of the three zones the saturated hydraulic conductivity and the moisture contents at saturation, wilting point and when air dry, should be entered. To compute the overland flow on soils without a hardpan the curve numbers for the SCS runoff equation need to be known. Finally the depth of each of the three zones (including the depth to the ground water) has to be specified. The soil hydrologic data may be found in the more recent S.C.S. soil surveys. The curve numbers are tabulated in most hydrology textbooks. Pan evapora- tion is measured at several locations in each state. The output consists of a disk file containing the amounts of infiltra- tion, the percolation and the recharge to the ground water. These amounts are summarized over a user-defined time interval. The file is used by the Vadose Solute Transporter. During the execution of the program the recharge, infiltration and evapotranspiration are shown graphically on the monitor. As an example Figure 2 gives a printed copy of what appears on the screen. cm Water Balance Summaries J F M A M J J A S O N D incremental 2 1 ° cumulative 150 ~0 -, 0 .~- Inflow [] Evap ~ Recharge ~ Figure 2. Display of Screen of the Vadose Water Balancer. Methods Used for Simulation The theory on which the Vadose Water Balancer is based is treated in som~ detail because of the importance of mass flow in the transport of chemicals in the soil profile. To meet the design criteria for n~nagement models as set forth earlier -- permitting long-term simulation and requiring that input data is generally available from published sources~ the description of the soil is kept simple. The soil profile is broken up in four zones as shown in Figure 3. The first zone is the active evaporation zone (ZONE l) and has a depth which changes with time in response to the seasonal plant growth, Below the evaporation zone and at a constant depth is the boundary between the upper and lower transmission zones (ZONES 2 and 3. respectively). The distinction is made for computational reasons as well as to provide sensi- tivity to properties of different soil horizons. Below the lower (unsatu- rated) transmission zone is the saturated zone or aquifer. The unsaturated movement in zones 1 through 3 is vertical while the flow of water and solutes in the saturated is mainly in the horizontal direction. Zone I ] Active Evopo- '~lraznSo~r°ti°n J Zone 2 Upper tronsmission zone Zone ~3 Lower Ironsmis$ion zon~ · Soturoted zone or impermeable Ioyer Figure 3. Division of Soil Profile for Vadose Water Balancer. To facilitate long-term simulation a variable time step is used. The time step is short (less than one day) when the profile is wet and the fluxes are high. The time step is large when the profile is dry and fluxes are small. The meximum time step is set in the program and depends on the interval over which the fluxes are summarized and is always smaller than two consecutive rainfall events. The main components of the Vadose Water Balance are shown in Figure 4. The overall structure is in essence very similar to other functional as well as mechanistic models such as ARM (Donigan and Crawford, 1976), DRAINMOD (Skaggs, 1979), CREAMS (Kuisel, 1980), ANSWERS (Beasley, 1977) and CPM (Haith, et al., 1984) but the methematical basis of most of the components is very different from these earlier models. The water mass balance equations for each zones are: Zone 1: dlt Am! = Rt + It + Mt - Qt - Et -Plt (7) Zone 2: d2t Am2 = Pit- P2t (8) Zone 3: d3 Am3 = P2t - P3t (9) where dlt, d2t and d3 are the thicknesses of each zone (em). dlt and d2t vary with season, but d3 is constant; see figure 3. Ami is SHALLOW PECOLATION MOISTU~[ · INT[RF~OW ZONE 2 RECHARGE.O WHEN THE SOIL HAS AN ~MPERMEAgLE LAYER INTERFLOW.O WHEN THE SOIL IS HOMOGENEOUS AND DEEP Figure 6. I DEEP PERCOLATION I SOl L MOISTURE · INTERFLOW ZONE IRECHARGE GROUNDWATER OR IMPERMEABLE LAYER Hydrologic Model Structure. the change in moisture content per unit volume in layer J. Rt is the rainfall (tm), It is irrigation (tm), Mt is enowmelt (tm) and Qt is runoff (tm). Et is evapotranspiratton (tm), which occurs only in Zone and Pjt is the amount of water conducted downward through Zone J. Snowmelt is computed by a degree-day factor (U.S. Army Corps of Engineers, 1960). Irrigation is applied whenever soil moisture falls below some specified limit provided that the elapsed time since irrigation is greater than or equal to the length of the irrigation cycle. In applying equations 7-9, water inputs to each soil zone, which include rainfall, irrigation and snowmelt for Zone 1, are assumed to infil- trate at a ~uch higher rate than that of evapotransptration and percola- tion. In this model water is added at the beginning of the timestep and then allowed To redistribute slowly during the timeetep as a function of moisture content. Unlike precipitation, irrigation, snowmelt and runoff, evapotranspira- tion and percolation are modeled as continuous processes occurring 10 throughout the time step. Because simultaneous evaporation and percolation occur in Zone I while only percolation occurs in Zones 2 and 3, a separate mathematical treatment is required for the active evaporation and transmis- sion zones. Thus, water movement in Zone 1 is discussed separately from Zones 2 and 3. Evaporation and percolation are slow continuous processes. 8otb rates depend on the volumetric moisture content of the soil. When the moisture content is greater than or equal to mrsd (a meisture content below which no do~raward flow occurs in the root zone), evapotranspiration occurs at its potential rate, Pg. For the model, potential evaporation was estimated by a simplified Penman Equation (Merva and Fernandez, 1982; Norman, 1984) or if no daily evaporation data are available by PE: ( PEJul - PEJan) { I + sin (<d - 100) x ~ )} + PE%an <10) 2 180 where PEjan and PEju1 are the average potential evapotranspiration during January and July respectively. At moisture contents above mred percolation is significant; below Mred moisture content of redistribution, hydraulic conductivity is so small that downward flow is assumed to be zero. Stated mathematically, the relationships for redistribution following rain, malt, and irrigation inputs to the root zone are as follows: d1 dml = -PE - k(ml), for m1 > mred (11) dt dI dml ~ -E(m1) for m1 ~ mred (12) dt where m1 is the updated moisture content in Zone I taking into account the water added by precipitation, snowmalt and/or irrigation, k(m1) is the unsaturated hydraulic conductivity at moisture content mi, PE is the poten- tial evapotranspiration and E is the actual evapotranspiration rate. Equation Il is based on the assumption proposed in Baver, et al. (1972) that gravity dominates over matrlc forces making the hydraulic potential gradient close to unity. For the unsaturated conductivity in equation 11, an exponential relationship was used because of the necessity to obtain closed form solutions; e.g. k(m1) - ks exp[~(&-l)] (13) 9 is the reduced moisture content, given by: e = (14) ms - md In the above equations k(m) is the hydraulic conductivity at moisture content m (cm/day), ks is the saturated hydraulic conductivity (cm/day), 11 md is the air dry moisture content, and K is a constant which is based on the work of Bresler, et al. (1978) has an universal value of 13. Assuming that the moisture content decreases uniformly with depth when the soil is drying out, equations 11 and 13 can be combined to solve for t', the time it would take for a given moisture content, ml, to be reached starting at an initial moisture content for Zone I of mil. t' = -d1 [ml - md - ms - md In [P~ + ks (exp(-m))(exp(~01))]] + t*(15) PE PE ~ where t* is an integration constant determined by the initial conditions expressed by the equation below: t*~ dx [~il - md ms - md PE PE ~ In + ks (16) When moisture content was smaller than mrsd the downward flux was assumed zero amd the equatlon for the actual rate of evapotranspiration assumed a linear relationship between the actual evapotransptration rate and the moisture content between the moisture where soil water first limits evapotransptratton, mred, and the moisture content where the soil is so dry that evapotransptration ceases, mwp. Note, mwp is the permanent wilting point and not necessarily equal to md, the air dry moisture content. Stated mathematically, w! E - ~ PE (17) W where w1 = dl(mI - mwp), or moisture in layer at any time (tm), w* ~ d1 (mrsd - mwp). Rearranging equation 17, substituting it in equation 12 and solving the differential equation, the evapotranspiration under water limiting conditions is obtained. Et = dl(mil - m~p) (1 - exp ( - PE At)) (18) dl(mred - mwp) The corresponding moisture content is given by dlmtl - Et mi = (19) dl Beneath the active evaporation zone there is no plant uptake of water, and the flux of water is equal to the hydraulic conductivity (assuming unit hydraulic gradient). 12 dj dm3 = -k(mj) (20) dt where the subscript J refers to Zone 2 or 3. The relationship for k(m) (equation 13) is substituted into equation (20) and the resulting equation is solved by separation of variables to yield an expression for the water content as a function of time and initial water content. msj - mdj [ksj exp(- )At + exp(- eij)] mj = mdj ~ dj(msJ - mdj) (21) Moisture content at the end of the time interval can be found by substituting the length of the time interval for At in equation (21). In the unsaturated zone below the root zone, the downward flow of water Pit is simply equal to the change in moisture content over the interval ti~es the thickness of the soil zone. rjr ~ dj{mj - mlj) (22) where mij is the initial moisture content in Zone J. The overland flow is calculated with the modified SCS curve number method. In addition runoff was assumed to occur on the shallow soil when the profile was saturated (i.e., when no water can infiltrate into the soil). Thus, when the soil was saturated the curve number Jumps to almost 100 (i.e., all precipitation becomes runoff). The Vadose Water Balance also has a component that calculates inter- flow in soils with a hardpan. 13 The Vadose Solute Transporter The Vadose Solute Transporter simulates the movement and the attenua- tion of chemicals within the unsaturated zone above the water table. The submodel includes the processes of first order degradation, dispersion and diffusion as well as mass movement. The input of the model consists of a file generated by the Vadose Water Balancer. This file contains not only the fluxes of water such as infiltration, percolation and recharge but also common hydrologic soil parameters that are shared between the two models such as the equilibrium moisture content for the three zones as well as the depth of each zone. Additional data required for this submodel are the application dates, amounts and incorporation depths of the applied contaminants. Adsorption parameters for the root zone and lower transmission zones are also needed. Finally, the degradation rates as a function of depth from the surface are required to run the submedel. The output consists of a dynamic display of the solute concentration pattern over the depth of the vadose zone (Figure 5). The contributions of the chemical to the ground water are written to a file on the disk for use by the Aquifer Water and Solute Transporter. CONTAMINANT MOVEMENT FROM THE LAND SURFACE TO THE AQUIFER Year 2 Day 293 Conc. at aquifer 362 d e 6 Land surface 0 (solute, g/cm ha) 300.0 14 Figure 5. Screen Display for the Vadose Solute Transporter. Methods Used for Simulation The simulation of the movement of the solutes through the soil profile is complicated by the adsorption-desorption and decay of pesticides as well as the simultaneous convection and dispersion. Mechanical modeling approaches solve the exact differential equation describing these processes with a finite difference or finite element technique. This technique is time consuming considering the intended uses. In this submodel the simula- tion is simplified by decoupling the transport from the degradation. In the discussion in the following section this two step approach is maintained. The movement and dispersion are discussed first followed by the attenuation of the chemical. The solute movement is superimposed on the water movement and is partly based on simplifying assumptions proposed by Gardner (1965) and Rao et al. (1976, 1981). The depth of the solute peak is calculated first and then the dispersion of the s61ute around the peak location is obtained with an analytical solution to the convective-dispersive transport equation as original proposed by Scheidegger (1960) and used by Walter (1974). The program keeps track of the peak location for each of the pesticide messes that are applied. The depth of the solute peak may be found as (Gardner, 1965; Rao et al.,1976); zt = zt-1 + AP (23) meq + Kd P where zt_1 is the location of the pesticide at time "t-l", zt is the location at time t, meq is the moisture content at equilibrium and may be found from equating the average yearly flux to the unsaturated conductivity in equation 13, AP is the quantity of water flowing past the peak and is equal to the average of the in-out going fluxes of the zone in which the peak is located. Kd is the adsorption partition coefficient (based on a linear adsorption isotherm) and p is the density of the soil. The solute dispersion around the peak is found as M(x) = 0.5 Mo erf x 2/ 8 zt (2~) where M(x) is the mass of the chemical between the peak and a distance x from the peak. Mo is the total amount of pesticide. ~ is the dispersivity divided by the average downward velocity of the peak. Walter (1974) found that a value of three for ~ fits most conditions, zt is the distance of the peak from the surface. Thus z ia defined in the Euclidian coordinate system. The x coordinate is used in a moving coordi- nate system with an origin at the peak location. This coordinate system moves at the velocity of the peak as calculated with equation 23. The first step in the computational procedure is to obtain the flow of water through each zone simulated by the Vadose Water Balancer. The flux is then substituted in equation 23 to find the solute peak location. On the day of pesticide application, the pesticide peak is at a depth equal to the incorporation depth, where it remains until the first precipitation or irrigation event. With the first infiltration event, the peak location is determined by equation 23. When the pesticide concentration with depth is displayed, the position of the band is substituted into equation 24 t.o find the distribution of the pesticide around the peak. After distribution several pesticide applications may come to overlap. Contributions of ail pesticides at each depth interval are summed and displayed. The process is shown schematically in figure 6. When the pesticide band is near the surface, it is possible that equation 24 will give a positive mass of pesticide above the surface. When this happens the portion above the surface is repositioned equally between the peak and the surface. The preceding equations do not account for degradation of the pesticide. As the pesticide is in the soil, however, the concentration decreases due to break down of the molecule. The rate of break down decreases with depth. In the root zone the degradation is higher than in the lower transmission zone. In the root zone, breakdow~ is microbial in character while in the subsoil hydrolysis takes over in importance. Usually microbial degradation is much more effective than hydrolysis. To account for varying rates of degradation with depth the pesticide mass for each application is divided up in portions with the same percent- age of equal mass in each. Each pesticide mass is divided up in a maximum of 10 portions. The division occurs shortly after application. During the solute Journey the pesticide mass remaining in each of these original partitions is being tracked. This is accomplished by calculating the location of the partition and then finding the degradation rate for the 15 Figure 6. PESTICIDE CONCENTRATION Schematic of Summing up of Separate Pesticide Bands. pesticide at that location. Assuming first order decay, the mass of pesticide in any partition at any time is given as Mpt = Mpo exp[-( kxt! + k2t2 + .......... + kntn)] (25) where Mpt is the mass of the pesticide in partition p after elapsed time t, MpolS the original amount of pesticide present in partition p at time of application, tj is the length of time the pesticide in the partition has a decay rate of kj. The degradation rate kj is found by averaging degradation rates over which the portion extend~ itself. In the beginning of the program the degradation rates for each of the partitions associated with various peak locations are calculated and stored in memory as a lookup table. The advantage is that during the simulation, there is no need to distribute the pesticide around the peak for each time step to find the degradation rate, k, for each partition; but, by finding the nearest peak in the lookup table, the degradation rate for each of the partitions is known too. This process significantly reduces the execution time. As stated before, the pesticide input to the ground water is is one of the output variables written to a disk file. To calculate this an imaginary line is placed at the depth of ground water table. The soil profile is thought to extend below this imaginary line. By setting the pesticide degradation rate to zero below the imaginary line, and keeping track of the total pesticide amounts below the line, the quantity of pesticide passing into the ground water is equal to difference in total pesticide amounts below the line. 16 Aquifer Water and Solute Transporter The Aquifer Water and Solute transporter simulates the movement of water and the movement and degradation of a solute in a vertical, two- dimensional cross-section of an unconfined aquifer. The water flow is based on steady-state, Dupuit fl~w assumptions. The chemical movement is superimposed on the water flow and includes first-order degradation and linear adsorption/desorption. The input consists of recharge and solute inputs aa a function of time and is obtained from a file on the disk, created I~ the Vadose Solute Transporter. Other input data required are the characteristics of the aquifer consisting of the width of the cross-section, elevation of water at outflow point, locations of flow path starting points, and the saturated, hydraulic conductivity in the horizontal and vertical direction. The chemical degradation and adsorption characteristics are also needed. The output consists of solute concentration over time and along one flow path and is displayed on the screen. An example is given in Figure 7. t7 SOLUTE TRANSPORT IN THE SATURATED ZONE Solute concentration (ug/l} Flowpath 1 2' 0--['--~~ ~~ ~--400 I sochrone Clock: ~ 9.9yr. th Cell: Conc.: (rr -----:-- 0.0 Max age is 22.2 years 0 distance (m) 1000 Figure 7. Screen Display for Aquifer Water and Solute Transporter. Methods Used for Simulation The elevation of the water table in a two-dimensional, steady-state ground water flow pattern is calculated using the hydraulic approach of Dupuit and Forchheimer as expressed by Gelhar and Wilson (1974). The boundary conditions (see figure 7) include a hollow boundary at the left and an impervious layer at the bottom, uniform recharge along the top, and a constant-head outlet at the right. The equation below gives the height of the water table at any location: 18 -- /{ h°2 + q y (2 L - y) head(y) ) (26) ks where head(y) is the elevation of the water table at position y measured from the impervious left edge, ho is the constant head at the right edge, q is the recharge rate averaged over the period of simulation, Ks conductivity, and L is the width of the cross-section from the left to the right edges. Flow directions and speeds are represented by plotting streamlines and isochrones. Flow paths are plotted using the equations: y(t) = L + (Yo - L) ex~ [~otI (27) z(t) = head(yo) exp [~ot] (28) where q, ho, and L are as defined above, t is a "travel time" measured from the entry of a "tracer particle" at the water table, Yo is the location where the path intersects the water table, y(t) is the horizontal location of the particle at time t, and z(t) is the vertical location of the tracer particle at the same time. A whole path may be plotted by varying the parameter t up to the point where y(t) is over the right boundary. An isochrone Joins the points on adjacent flow paths that correspond to the same t value. Isochrones are plotted such that they reflect the solute's lagged movement (due to adsorption) rather than the water's movement. Chemical movement and degradation are calculated for successive time intervals. It is assumed that there is no chemical in the system at the start and that there is no dispersion of the chemical as it moves. The equations used to represent the effects of chemical adsorption and degradation are equivalent to those used in the Vadose Solute Transporter. They are far simpler due to the omission of dispersion. A "band" of chemical which enters in one time interval thus stays together until it exits from the saturated system at its downstream end. Degradation reduces the m~ss in the band depending only on the amount of time since the band entered the saturated zone. Realism A mathematical model must be realistic to be used in practical appli- cations. The degree to which it should simulate the reality depends on the intended use. If a model mast reproduce day to day variations in measured field observations for research purposes, the simulation should represent the reality for the particular area in detail. However, a model used for for training, diagnosis of contamination problems in the soil and/or screening out combinations of soil chemical and climate posing an environmental problem ~st be qualitatively realistic. More important the latter kind of the model (such as MOUSE) must perform reasonably over a wide range values, rather than be specialized within tight limits. In all cases, comparisons of a model's predictions with field measure- ments provides an indication of consistency and accuracy. Recognizing the variability in field measurements and the limited capacity of management models to represent this variability, no attempt has been made to compare statistically the observed versus the real data. Instead, for various experiments, the predicted and observed data are shown and it is up to the user to judge how mach confidence she/he is willing to invest in this tool MOUSE for a given application. Great variability in solute and water flow has been observed in the vadose zone. For a summary of these observations see for example Rao and Wagenet (1984). However, in predicting groundwater concentration the spatial distribution of solute movement in the vadose zone is of less concern. Water resides long enough under the field that the variation in arrival time of a pesticide mass (as affected by the variability in the vadose zone), are negated by the the dispersion and diffusion in the saturated zone, thus resulting in averaging of the many variations intro- duced in the unsaturated zone. Temporal trends on the order of the travel time in the saturated zone under the field rm~st be preserved in the simula- tion results. Consequently, management mudele, which are concerned with the eventual fate of pollutants in the aquifer, predict areal average concentrations and fluxes based on their intended use. The submodels are tested separately. For each model physical measured data are used. If they do not exist data are estimated based on data avail- able for other similar areas. In all field cases historical weather records Climate Generator Picketing (1982) tested the algorithm for precipitation and air temperature for 25-year periods at weather stations in New York (Aurora), Iowa (Ames), and Georgia (Athens). The validation is described in more detail by Haith and others (1984). The soil temperature equations have been validated by Steenhuis (1979). Test results for the Climate Generator submodel are presented in these references. The Vadose Water Balancer This submodel was tested against precipitation and recharge or deep percolation (measured at the 90 depth) data for a site on the grounds of Cornell University's Horticultural Research Farm located 4 km north of Riverhead, N.Y. and 1 km south of the Long Island Sound. The farm lies on a glacial outwash plain with soils typical of the region. Soil at the site is a Haven sandy loam. The soil profile has a sharp distinction between the top 30 cm and the rest of the profile. The plow layer has up to 40% clay and silt and has been compacted by traffic. The soil below 30 cm is a yellowish-brown, gravelly sand with less than 10% silt and clay. The sand has a high saturated conductivity of approximately 10 m/day. 19 The direct and the closure methods were used to measure recharge. The direct method consisted, first, of measuring simultaneously the matric potential and moisture content at 90 and 120 cm depths. By relating moisture content to hydraulic conductivity the vertical flux of water could be calculated directly with Darcy's Law. The closure method calculated recharge from the hydrologic budget equation with evaporation computed from the mtcrometeorologic data. A detailed description can be found in Steenhuis, et al. (1984). Recharge at the site was measured by both methods in 1980 from January through June and during November and December. Precipitation and minimum and maximum temperature were measured at the site. Average monthly cloudi- ness index and wind speed were taken from the airports in New York City. Soil parameters used in the simulation model are given in Table I and were measured at the site itself (Steenhuis, et al., 1984). The simulated recharge as well as the observed recharge with the direct method are compared in figures 8 and 9. The results of the direct method were assumed to be more accurate because the recharge estimate does not contain the errors in the measurement of individual components of the Figure 8. Precipitation and Predicted and Observed Recharge on a Haven Sandy Loam from January through June 1980. hydrologic budget as with the closure method. The closure method measured a 10 to 20% higher recharge than the direct method except for January and December when the closure method was much higher than the direct method. As can be seen from Fig. 8 and 9 the simulated recharge was generally slightly higher than the directly measured recharge. The difference between the direct method and the simulated recharge on a month by month basis was always less than the difference between the direct and closure methods. The model therefore gives good results of recharge based on physical measured data. 2O 21 Table 1. Soil Parameter Values for Water Balance. Zones 1 and 2 Zone 3 Saturated hydraulic conductivity (m/day) Saturated moisture content (cmS/tm3) Moisture content at wilting point (cmS/tm~) Moisture content at which actual evapo- transpiration is smal}er ~han potential evapotranspiraton (cm /tm~) Dry moisture content (cm~/cm~) Depth of zone (m) 0.6 0.50 0.08 0.24 0. O05 0.26 10.0 0.45 0.005 0.64 *Not applicable. Figure 9. "1 ..... ..=.- Precipitation and predicted and observed recharge on a Haven Sandy Loam during November and December 1980. The model was also tested for watersheds with soils underlain by a hardpan at shallow depth in upstate New York. Predicted and observed flows from watersheds up to 100 sq km agreed well (Norman, 1984; Steenhuis et al., 1983). Vadose Solute Transporter The Solute Transporter was tested against a laboratory soil column experiments, a mechanistic model and soil cores taken under two potato fields on Long Island. [n the laboratory columas the distribution of radioactivity with depth was measured from radio-labelled butylate, alachlor, and metolachlor after they were leached with 15 cm of water. The application rates ~ere equiva- lent to 4.5. 2.5 and 2.5 kg/ha, respectively, and the adsorption partition coefficients were 3.14, 1.8 and 1.98 cc/g respectively. The soil used was a Keeton sandy loam. More information may be found in Spillner et al. ([983). To test the Vadose Solute Transporter the 15 cm of recharge as well as pesticide and soil characteristics were supplied as input param- eters to the model. The comparison of predicted and observed distribu- tions for the three pesticides are shown in figure 10. A satisfactory agreement was obtained for these pesticides. Although metolachlor had approximately the same characteristics as alachlor, its observed distribution with depth was different. The model underestimated slightly the movement of matolachlor. P~STICID~ CONC£NTRATION (MG/KG) O,S 0,9 1.5 2.1 2.7 0 0 0 BUTYLATE 22 j 0.3 0.6 ~. 20 ALACHLOR 0.9 1.2 1.5 10 20 O 0.~ 0.6 0 METOLACHLOR 0.9 1.2 1,5 Figure 10. SIMULATED PESTICIDE CONCENTRATION 0 OBSERVED PESTIC[DE CONCENTRATION A comparison of Predicted and Observed Distribution of Butylate Alachlor and Metolachlor. The second test of ~OUSE compared the model against the predictions of a mechanistic finite difference model developed by Intern (1980). The model was used to predict aldicarb leaching under a potato field on Long Island. The field was located in Cutchogue, NY, which is within a intensive potato growing area on the North Fork of Long Island. Biannual aldicarb applica- tions were made from 1977 through 1979 at a total rate of 3200, 4000 and 5000 grams of active ingredient per hectare per year for the three years. Five cores were taken for sampling in December, 1979. The soil was compa- rable to the soil on the Horticulture Experimental Farm as described before. The depth to ground water was 2.5 m. Intera calibrated their model and found that a half life of 77 days in the whole profile and an adsorp- tion partition coefficient of 0.16 cc/g best fitted the observations iu two of the five cores. In MOUSE we substituted the same parameters (including the same weather information from the Greenport Weather Station). As can be seen from figure 11 we obtained almost a complete match between the aldicarb contents simulated b~ the t~o models. This is not surprising as both models are based on the same original mathe~atical formulations. It also implies that one can substitute simpler functional models for mecha- nistic models when the system's parameters are not very well known, The Mouse simulation run was done on a micro computer in less than 15 minutes including the time it took to enter the data, The finite difference approach required a larger computer and considerable expense, 23 0 ,, ALDICARB CONCENTRATION ( ~G/L) 20 ~0 60 80 100 120 1~0 ~L2. O~ o I 2,aJ ol 0 I O SIMULATED ALDICARB CONCENTRATION BY INTERA (1980) SIMULATED ALDICARB CONCENTRATION BY ~0US[ Figure 11. Comparison Between the Intera (1980) and MOUSE Simulation of Aldicarb Leaching. The third and most rigorous test was for soil cores under potato fields taken in December 1983 at t~o locations on Long Island. The cores were analyzed for aldtcarb content b~ the Union Carbide Corporation (Hughes and Porter, 1984). The first location was near the site of the Intera study. The depth of the groundwater was approximately 10 meters. The second site was near Wading River with a depth to groundwater of 31-33 meters. In both cases the soil was very similar to the Haven soil at the experimental farm. The aldicarb applications were made from 1975 through 1979 after which the pesticide was withdrawn from the Long Island market. During each year an application of 3000 g AI/ha was made at planting. In addition during the last three years a later application of aldicarb was made during June at a rate of 1200 or 1800 g AI/ha. The greatest uncertainty in modeling aldtcarb is its rate of degrada- tion in the vadose zone and saturated zone. Unfortunately, laboratory and field experiments giving the rate of degradation are few, and the results are inconclusive. A previous review summarizing studies on aldicarb degra- dation in a variety of soils showed ranges in half lives from 1 to 231 days (Kain and Steenhuis, 1984). In our simulation a 45 day half live was used for the root zone. This was increased to 90 days for the next 50 cm to reflect the decrease of microbial activity with depth (Pacenka and Porter, 1981). For the remainder of the vadose zone, a half life of I0 years was used. This was obtained from work of Lemley (Personal communication, 1983) and analysis of aldicarb content data from a deep soil core taken at the Long Island Horticulture Research Laboratory (unpublished data). The adsorption portion coefficient was set at 0.10 in the root zone and zero in the subsoil. The numbers were calculated from a linear regres- sion relating adsorption coefficients to soil organic matter derived from the results of four field and laboratory experiments. (Kain and Steenhuis, 1984). Based on the above pesticide properties and soil characteristics similar to those used for the recharge study, the pesticide distributien with depth was simulated. For the field on the North Fork predictions agreed with that what was observed: i.e. all aldicarb had moved out of the unsaturated zone (into the ground water). For the field near Wading River the model and the observed concentration started near the same depth. (Figure 12). The variation in concentration with depth was larger for the predicted than the observed results. The dispersion parameter which deter- mines the spread of the band was based on experiments done with laboratory columns. It has been noted that in ground water the dispersivity tends to increase with distance. Thus the simulation was repeated with a ~ value (equation 24) increasing linearly fro~ 3 at 2 m to 10 at 30 m. The result- ~ng prediction fitted slightly better. Overall, as long as no better information about the subsoil parameters is available, the simulation results have to be considered acceptable. The Aquifer Water and Solute Transporter The aquifer n~del was tested for two pesticides for a transect on the North Fork of Long Island, near Depot Lane. In 1981 the Suffolk County 24 ALDICARB CONCENTRATION (~,G/I(G) 8 12 .. 16 20 2~ 25 0 0 --- DISPERSION PARAMETER INCREASING NITH DEPTH DISPERSION PARAMETER CONSTANT WITH DEPTH 0 OBSERVED ALDICARB CONCENTRATION ~N T~O CORES Figure 12. Comparison of Simulated and Measured Aldtcarb Content of a Potato Field near Wading River. Department of Health Services drilled a series of geological test holes and observation wells along Depot Lane in orde~ to obtain information about how aldicarb and other contaminants move through the North's Fork aquifer. The transect runs from the Long Island Sound to the Cutchogue harbor and runs parallel to the ground water flow. Along the transect the water table height, aldicarb and dichloropropane concentrations were measured in a time span from spring 1981 until spring 1982. Additional information about the sampling procedure and results can be found in Baler and Robbins (1982). The prediction of the water table height is first compared with the observed water table height. The hydraulic conductivity was taken as 90 m/day horizontal with a horizontal-to-vertical anisotropy ratio of 5.0 (Baler and Robbins, 1982). The depth of the aquifer was taken as 33 m near the Long Island Sound and 40 m near the Cutchogue Harbor. The predicted and observed water table height are compared in figure 13. The predicted water table height is a "steady state" height based on the average recharge during a nine year simulation period from 1975 - 1983. The variation in observed ground water was the difference between the readings in the summer of 1981 and following wLnter. As expected the ground water levels near the salt water interface were slightly off. The simplified flow equation employed with MOUSE does not account for the interface between salt and fresh water and the upward curving of streamlines near the interface. This causes a higher loss of energy than predicted with the Dupuit assumptions and results in a higher water table than predicted. I I 1.0 0,5 0 LONG Figure 13. 1000 2000 3000 4000 COTCHOQuE HARBOR DISTANCE (M) Observed and Simulated Water Table Height for Depot Lane Transect. For the aldicarb simulation pesticide application data along the transect by Trautmann et al. (1983) were used. The degradation and adso~p- tion constanta for aldicarb were the same as used in the Vadose Solute Transporter test near Wading River. Soil parameters were equal to those used in the Vadose Water Balancer validation. The model simulations in figure 14 revealed that the depth over which aldicarb was found in the ground water resembles closely the calculated distribution. The model predicted a penetration of approximately 6 meter below the ground water table. Aldicarb had only been found below this depth in a few locations by 1981 (Figure 14). The comparison between predicted and observed concentra- tions was hampered because the exact locations and depths of aampled wells were not known. In general the model predicted concentrations ranging from 10 ppb to 50 ppb with a very few exceptions when the concentratione exceeded 100 ppb. In general this was at the same locations where high concentrations were observed in the ground water. The final test involved a comparison of calculated times of ground water travel on the North Fork since the early 1950's. Dichloropropane, a soil fumigant which has been detected in the ground water in the area was first used in 1951 in the Depot Lane transect and can serve as tracer. Assuming that as in case of aldicarb the adsorption partition coefficient is zero, the observed distribution of dichloropropane (figure 15) is roughly consistent with the ground water travel times predicted with the Aquifer Water and Solute Transporter. This lends additional confidence to the applicability of MOUSE to this kind of setting. 26 27 ( ,,T,A } ~0 ~ ,T,¥/~'~~r 28 (,~,J) i(o z &¥A.1"l Conclusions Several tests of simulation results against observed field and laboratory data all indicate that MOUSE can reproduce the general measured patterns of subsurface water and selected pesticide concentrations quite well within the limits of knowledge of field conditions. This degree of realism is adequate for planning and management applications and ensures that MOUSE users will obtain simulation results that are quantitatively reasonable for the chemicals examined. As with most models of the functional, management type, MOUSE's realism is coarse. Only with fine- tuning of parameters can it reproduce short-term small-scale details of a particular field or laboratory experiment. This suggest that our intended restriction8 -- to training and management uses -- are appropriate. 29 References Addtscott, T.M. and R.J. Wagenet. 1984. Concepts of Solute Leaching in Soils: A Review of Modelling Approaches. Journal of Soil Science (in press). Baler, J.H. and S.F. Robbins. 1982. Report on the Occurrence and Movement of Agricultural Chemicals in Ground Water: North Fork of Suffolk County. Suffolk County Department of Health Services, Bureau of Water Resources. Baver, L.P., W.H. Gardner and W.R. Gardner. 1972. Soil Physics. 4th Ed., John Wiley, NY, NY. Beasley, D.B. 1977. ANSWERS: A Mathematical Model for Simulating the Effects of Land Use and Management on Water Quality. Unpublished Ph.D. Thesis, Purdue University, Lafayette, IN. Biggar, J.W. and D.R. Nielsen. 1976. Spatial Variability of the Leaching Characteristics of a Field Soil. Water Resour. Res. 12:78-84. Bresler, E., D. Russo and R.D. Miller. 1968. Rapid Estimate of Unsatu- rated HYdraulic Conductivity Function. Soil Sci. Soc. Am. J. 42:170-172. Donigan, A.S. and N.H. Crawford. 1976. Modeling Pesticides and Nutrients on Agricultural Land. EPA 600/2-76-043. U.S. Environmental Protection Agency, Athens, GA. Gardner, W.R. 1965. Movement of Nitrogen in Soil. IN: Bartholomew, W.V. and F.E. Clark, editors: Soil Nitrogen. Agronomy, A Series of Monographs, V. 10. American Society of Agronomy. Madison, WI. Gelhar, L.W. and J.L. Wilson. 1974. Ground Water Quality Modeling. Ground Water, 12:399-408. Haith, D.A., L.J. Tubbs and N.B. Picketing. 1984. Simulation of Pollution by Soil Erosion and Soil Nutrient Loss. PUDOC, Wagntngen, The Netherlands. Hughes, H.B.F. and K.S. Porter. 1984. Tracking Aldtcarb Levels in Long Island Ground Water. Unpublished. Water Resources Program. Center for Environmental Research. Cornell University, Ithaca, NY. Intera. 1980. Mathematical Simulation of Aldicarb Behavior on Long Island. Unsaturated Flow and Ground Water Transport. Inters Environ- mental Consultants, Inc., Houston, TX. Kain, D.P. and T.S. Steenhuis. 198&. Adsorption Partition Coefficients and Degradation Rate Constants and Half Life of Selected Pesticides compiled from literature. Department of .Agricultural Engineering Professional Paper 84-3. Cornell University, Ithaca, NY. Knisel, W.G. 1980. CREAMS: A Field-Scale Model for Chemicals Runoff and Erosion from Agricultural Management Systems. USDA Cons. Res. Rep. 26. Merva, G. and A. Fernandez. 1982. Simplified Application of Penman's Equation. ASAE Paper No. 82-2013, American Society of Agricultural Engineers, St. Joseph, MI. Norman, W.R. 1984. Drought-flow Analysis and Prediction in Small Water- sheds. M.S. Thesis, Cornell University, Department of Agricultural Engineering, Ithaca, NY. Pickering, N.B. 1982. Operational Stochastic Meteorologic Models for Non- point Source Pollution Modeling. M.S. Thesis, Cornell University, Department of Agricultural Engineering, Ithaca, NY. Rao, P.S.C. and R.J. Wagenet. 1984. Spatial Variability of Pesticides in Field Soils: Methods for Data Analysis and Consequences. In review. Weed Sci. Soc. Amer. J. Rao, P.S.C., J.M. Davidson and R.E. Jessup. 1981. Simulation of Nitrogen Behavior in Cropped Land Areas Receiving Organic Wastes:33-37. IN: M.J. Frissel and J.A. vanVeen (eds.). Nitrogen Behavior of Soil-Plant Systems. PUDOC, Wageningen, The Netherlands. Rao, P.S.C., J.M. Davidson and L.C. Hammond. 1976. Estimation of Non- reactive and Reactive Solute Front Locations in Soils. In: Fuller, W.H. (ed): Residual Management by Land Dispos~l. Proc. of Hazardous Wastes Research Symp., Tucson, AZ. EPA 600/9-76-015. Scheidegger, A.E. 1960. The Physics of Flow Through Porous Media. MacMillan Co., NY. pp. 256-274. Skaggs, R.W. 1979. A Water Management Model for Shallow Water Table Soils. Report No. 134, Water Resources Research Inst., North Carolina Stats University. Spillner, C.J., V.M. Thomas, D.C. Takahashi and H.B. Scher. 1983. A Comparative Study of the Relationships Between the Mobility of Aiachlor, Butylate and Metolachlor in Soil and Their Physico Chemical Properties. IN: Fate of Chemicals in the Environment, American Chemical Society. Steenhuts, T.S., C. Jackson, S. Kung and W.G. Brutsaert. 1984. Measure- ment of Ground Water Recharge on Eastern Long Island. Journal of Hydrology (in press). Steenhuis, T.S., R. E. Muck and M.F. Walter. 1983. Predictions of Water Budgets for Soils with or Without a Hardpan. IN: Advances in Infiltra- tion. ASAE Monograph. Steenhuis, T.S. 1979. Simulation of Soil and Water Conservation Practices. Action in Controlling Pesticides. IN: D.A. Haith and R.C. Loehr (eds.), Effectiveness of Soil and Water Conservation Practices for Pollution Control. EPA 600/3-79-106. U.S. Environmental Protection Agency. Trsutman, N.M., K.S. Porter and H.B.F. Hughes. 1983. Southhold Demonstra- tion Site: New York State Fertilizer and Pesticide Demonstration Project. Unpublished. Center for Environmental Research, Cornell University, Ithaca, NY. U.S. Army Corps of Engineers. 1960. Runoff from Snowmalt. Manual 1110-2-1406. Washington, DC. 3O U.S. Environmental Protection Agency. 1984, A Ground Water Protection Strategy for the Environmental Protection Agency, Draft. Washington, DC. U,S, Water Resources Council, 1978, The Nation's Water Resources, 1975-2000. Vol. 1: Summary. Second National Water Assessment. Wagenet, R.J. and P.S.C. Rao. 19874. Basic Concepts of Modeling Pesticide Fate in the Crop Root Zone (in review), Weed Sci. Soc. Am. J. Walter, H,F. 1974. Nitrate Hove~nt in Soil Under Early Spring Condi- tions. Unpublished Ph.D, Thesis, University of WEsconein, Madison, WI. 3!