Dssamt Water Quality Model

Developed by

James T. Brock
and
Craig L. Caupp

Table of Contents

Introduction
Overview of DSSAMt
Hydraulics and Characteristics of the Channel
Hydrodynamic Features of DSSAMt
River Temperature and Solar Radiation
Boundary Conditions and Time Step
Benthic Algae Algorithm
Respiration of Periphyton
Primary Production
Respiration of Periphyton
Removal of Periphyton
Macrophytes
Nutrient Dynamics
Differentiation of Metabolic Processes in DSSAMt Based on Fluvial Characteristics
Data Requirements for DSSAMT Water Quality Model
Simplifying Assumptions Used in DSSAMt
References

Introduction

DSSAMt is a numerical model that was developed for simulation of water quality (primarily dissolved oxygen, temperature, and nutrients) in shallow rivers where benthic photosynthesis contributes an important part of total ecosystem productivity. DSSAMt includes the traditional "oxygen sag" approach for point loads such as BOD or ammonia that exert a direct oxygen demand on the receiving stream. What sets DSSAMt apart from other river quality models developed to date is the linkage of mechanistic processes for the growth and removal of benthic algae to the simulation of nutrients and dissolved oxygen. The model simulates competition between two assemblages of benthic algae, including nitrogen-fixing algae. The biomass of the two algal types are modeled dynamically as a function of nutrients, light, temperature, current velocity, and other environmental variables. A list of the parameters modeled in DSSAMt is given in Table. The linkages between nutrients, light, temperature, biomass, dissolved oxygen is illustrated in Figure ?hyper?. Because DSSAMt is dynamic with respect to some environmental conditions (solar radiation and other meteorologic constituents), but not with flow, it is classified as "quasi-dynamic." The model includes the processes of primary producer growth, decay, and associated downstream recycling of nutrients. The model is suitable for use in small to medium sized rivers where benthic production is an important oxygen source sink. DSSAMt has been used to examin water quality conditions under different flow and nutrient loading scenarios. The temperature model includes the ability to model topographic and vegetative shading.

Overview of DSSAMt

DSSAMt was designed to simulate water quality conditions in a river system where polluting substances enter the modeled reach from a variety of sources, including tributaries, point effluent discharges, surface water point and non-point runoff, groundwater, and leaching and scouring from the bottom sediments. DSSAMT provides a dynamic representation of diel (24-hr) variation in modeled constituent concentrations over the period of simulation. The model simulates competition between two assemblages of benthic algae, including nitrogen-fixing algae growing under conditions of low nitrogen. DSSAMt includes a routine involving heat transfer equations for simulation of water temperature based on ambient conditions. River pH is simulated through an evaluation of the carbonate equilibrium based on acidity, alkalinity, and uptake of carbon dioxide. The biomasses of algae are modeled dynamically as a function of nutrients, light, temperature, current velocity, and other environmental variables. The model is based on an assumption of steady flow conditions with exponential relationships used to estimate river hydraulics. The model has been applied to the Truckee River (Nevada) and the Red Deer River (Alberta).

The current version of the model DSSAMt, is a hybrid of two previously published stream models (SSAM V and LPSM), which has been enhanced with the addition of a module to simulate water temperature. DSSAMT had its origins in SSAM IV, Stream Simulation and Assessment Model: Version IV (Grenney and Kraszewski 1981) and LPSM, Lotic Periphyton Simulation Model (Runke 1985). SSAM IV is a steady-state model of water quality in stream environments that deals with both hydraulics and water quality, but it treats periphyton and aquatic macrophytes in only a rudimentary fashion. The LPSM model on the other hand, focuses specifically on the dynamics of the periphyton community. LPSM is dynamic with respect to biological responses to environmental conditions, but does not deal with stream chemistry or hydraulics, except as input data. For use in modeling water quality in the Truckee River, we grafted the LPSM periphyton algorithm onto a modified version of SSAM IV to create DSSAMT. The current version of DSSAMT, DSSAMt, includes a heat transfer submodel giving DSSAMt the capacity internally for prediction of water temperature. This enhancement facilitates assessment of lotic ecosystem response to alternatives involving management of river flow.

DSSAMT is both dynamic and deterministic, capable of simulating diel swings of all the water quality constituents modeled. DSSAMT was developed initially during 1985-1987 to investigate potential biostimulatory effects that various operational scenarios of the Reno-Sparks Wastewater Treatment Facility might have on the Truckee River, Nevada (Brock et al. 1989). DSSAMT initially emphasized simulation of pH, dissolved oxygen, and ammonia nitrogen (both total and un-ionized) because of the relevance of these water quality parameters on fish survival. The early applications of DSSAMT to the Truckee River did not involve river flows other than those observed historically, so observed water temperature conditions were entered as input data and not predicted. An assessment of potential effects of a flood control project on the Truckee River provided us with the opportunity in 1989 to develop a shade and water temperature model (separate from DSSAMT). The model included a detailed assessment of the effects of topographic and riparian shading on water temperature. In 1990, additional development of the river model involved the capacity to specify diel variations of constituents at the upstream boundary and a point load, and the incorporation of additional constituents (particulate phosphorus, soluble non-reactive phosphorus, particulate organic nitrogen, and soluble organic nitrogen) required for the assessment of total nutrient loadings to the modeled system. TDS and chloride were added to help track conservative substances.

During 1991-1992 DSSAMT was calibrated to 1989 Truckee River conditions and used to provide the State of Nevada's Bureau of Water Quality Planning with a technical basis for assessing water quality standards and wasteload allocation for point and non-point pollutant sources to the lower Truckee River. The calibrated model was used to evaluate the downriver ecosystem response to a series of nitrogen loadings emanating from municipal and agricultural sources. Our analysis focused on predicting growth and removal of benthic algae under varying nutrient loads and the associated impacts on dissolved oxygen regimes. These simulations provided a basis for revisions to the State of Nevada's water quality standards and the specification of allowable nutrient loadings to the Truckee River.

Hydraulics and Characteristics of the Channel

DSSAMt's hydraulic submodel begins with the river's headwater and proceeds downstream, conducting a flow balance by adjusting mainstem flow to account for distributed surface and subsurface flow, point load flows, and diversion flows. The model is based on an assumption of steady flow conditions with exponential relationships used to estimate river hydraulics. A sequence of steady flow discharges, each of which continues for a specified period of time, is used to approximate the flow discharge hydrograph. The model requires hydraulic geometry coefficients to calculate the average velocity, cross-sectional area, and hydraulic radius at specified points in the stream network.

The steady state flow assumption used in DSSAMt has the advantage of reducing the input data requirements and simplifying the flow and transport model requirements as well as hydraulic calculations. Flow conditions for each reach are held steady over the specified period of time Hyper. This assumption tends to be valid for rivers not subjected to significant variation in flow, or where flow variations are within a window of depths and velocity that result in similar growth conditions for the benthic algae. The benthic algae tend to integrate conditions during the pass. If instantaneous discharges within a modeled time period are sufficient for scouring of algae, the pass duration is adjusted so that it is short enough to allow for mean flows during the pass to be sufficiently high to allow for algal scour.

Longitudinal variation in depth and velocity within a reach is represented in DSSAMt by an adjustment for pools and riffles. Pools are defined as depositional areas where conditions are unsuitable for growth of benthic algae. Riffles are defined as erosional areas where conditions are favorable for the growth of benthic algae Hyper. The distribution and fluvial characteristics of riffles and pools varies can be expected to vary with changes in flow. As a general rule, pools constitute the reduced velocity (generally < 25 cm/s) part of the river and have a relatively quiescent, unbroken surface. Riffles tend to have surface agitation associated with flow over completely or partially submerged obstructions. In DSSAMt algal growth rates and biomasses are calculated for the riffle conditions with the simulated biomass for algal assemblages representative of the optimal values found in the riffles. The stratification of reaches into riffles and pools allows for differentiation of functionally significant ecosystem processes within the framework of a one-dimensional flow model.

DSSAMt may be applied to a river system with distributed surface inflows and/or outflows, and distributed groundwater inflows and/or outflows. The main program operates three distinct submodels in sequence: 1) system layout and flow balance, 2) water temperature simulation, and 3) simulation of water quality constituents. The first submodel (Hydraulic) starts with the headwater flow of the mainstream and proceeds downstream, conducting a flow balance by adding (or subtracting, as appropriate) distributed surface flow, distributed subsurface flow, point load flows, and diversion flows. The model calculates the average velocity, cross-sectional area, and hydraulic radius at specified points in the stream network and stores these data for later use. The Temperature and Water Quality submodels also start at the headwater of the stream, utilizing hydraulic data stored by the hydraulic submodel, and proceed downstream, solving the heat transfer equations and a system of differential equations to predict concentrations of the water quality constituents.

The concentration of the water quality constituents at any point in the river system are the results of two processes:

  1. the collection and physical transport of the substances from upstream sources by the flowing water; and
  2. the biochemical and physical reactions causing changes in concentrations or chemical composition during the time that the substances are being transported.

Each of these processes is simulated by the model. For the first process, the constituents entering the water are mixed with the main streamflow and transported downstream at the average cross-sectional velocity of the flow. The second process is simulated by biophysical and physical reaction kinetics. The constituents listed in Tables are presently included in DSSAMt. Figure 1 illustrates the relationships and linkages between these state variables in DSSAMt.

The model calculates diel constituent concentrations for constant flow conditions over a specified time period. Due to the steady state assumption associated with hydraulics and transport, it is preferable if the selected time period is not less than the travel time of flow through the sub-basin being modeled. We define the term "pass" to represent the length of time (in days) during which flows are held constant. During a single pass of the model, it is assumed that river flows do not vary significantly relative to total travel time. Steady state models based on a Cartesian reference frame (such as DSSAMt) are incapable of modeling the transport and flow dynamics associated with modeled periods characterized by variable flow such as storm runoff or releases below impoundments used for hydroelectric generation.

A model "simulation" consists of a single model pass, or a connected series of model passes. Passes of the river model are connected by using the final values for the first pass as initial conditions for the second pass; the final values for the second pass become initial conditions for the third pass, and so on. Thus, model simulations could vary in length from a single pass to an entire year. The concept of linking multiple passes during a simulation provides a mechanism for predicting biological responses--such as accumulations of benthic algae--which integrate conditions over an entire growing period.

The time span for a simulation of DSSAMt is specified for the application at hand. The program is structured so that time periods can be linked successively, and at the end of a period the program writes a set of final values for all constituents and model elements. This output data file for the period can then represent the initial values for a new time period. Accordingly, DSSAMt could be run for a day, a year, or many years of river time, depending on the particular management objective. The time step for the input of new meteorologic conditions during the model pass is determined by data availability. Options are available to account for diel variability (i.e., ten samples per day) in constituent concentration for upstream boundary conditions and for a point load such as a wastewater treatment plant.

Hydrodynamic Features of DSSAMt

River hydraulics are modeled for the one dimensional steady state condition. Reach average coefficients are used to estimate velocity, depth, cross sectional area, and topwidth within each reach. Channel hydraulic properties are defined by the relationships of average cross-sectional velocity to flow and hydraulic radius to flow by power equations first suggested by Leopold and Maddock (1953).

DSSAMt is presently configured to the main trunk of a river. Water quality of tributaries is not modeled; they are treated as point sources to the boundary of the modeled reach. If it were desirable to apply DSSAMt to a system that included tributaries, each tributary could be modeled as a separate river and the output saved to be used as the boundary conditions for the main stem of the river system. Alternatively, the DSSAMt computer code could be enhanced to allow simulation of river networks.

River Temperature and Solar Radiation

DSSAMt includes calculation of heat transfer equations, allowing the model to predict water temperature as influenced by atmospheric conditions. Weather conditions are input to the model, and used to calculate water temperature over time and distance based on net heat exchange between the river and the surrounding environment, including the bed. Calculation of net surface heat flux takes into account the effects of longwave and shortwave radiation, back radiation emitted by the water, evaporation and conduction between air and water, and energy conducted between water and ground.

The total solar radiation reaching an unshaded location at the river surface is calculated in DSSAMt based on latitude, longitude, atmospheric turbidity, and fraction of cloud cover. The fraction of incident solar radiation shaded by ridges and riparian vegetation may be calculated in DSSAMt as well. Solar radiation penetrating the water column to the river bottom is simulated using an exponential decay function that depends on turbidity. The size and erodability of bed material and river banks influence the relationship between flow and turbidity, which is specified using reach-specific relationships determined for the river under study. The fraction of total solar radiation that is photosynthetically active (PAR) is calculated based on short wave radiation using an empirically-derived coefficient for the study area.

Calculated solar radiation was used in all calibration runs. In order to determine the adequacy of the solar radiation algorithms, calculated solar radiation was compared to observed solar radiation. Data from the USGS Delta station located close to Pyramid Lake was compared to solar radiation calculated using cloud cover data from a location in the City of Reno. Figure ** illustrates both that the model reasonable estimated solar radiation and that daily variations can make it difficult for the model to match on each day. Cloud cover can vary considerably with distance down the river. Figure ** shows that on Julian Day 92 and 93, there was considerable cloud cover over the city of Reno reducing the calculated solar radiation, but not at the Delta station which measured maximum solar radiation for those two days. Since observed solar radiation was not available for all calibration time periods, calculated solar radiation was used for all model runs. The use of calculated solar radiation provided for a more consistent data set than switching from calculated to observed when observed was available.

The differences in air temperature along the Truckee River also added addition error into the simulation of water temperature. Figures ** - ** illustrate the wide variation in temperature between weather stations at Reno, Marble Bluff and SBARS. During August 1988, Reno typically had a much larger daily swing in temperature than did the Delta station (Figure **). This large variation in temperature lead to the development of downriver air temperature adjustment factor.

Boundary Conditions and Time Step

The exogenous parameters that affect the modeled system define its boundary conditions. Boundary conditions include flow and water quality characteristics at the headwater as well as point loads and dispersed sources. Boundary conditions which are input to the model as exogenous influences on river temperature include the following:

The time step for the input of meteorologic conditions during the model pass is determined by data availability. For the Truckee River simulations we made use of meteorologic and solar radiation data that were available once every hour. DSSAMt can account for diel variability (as many as ten times per day) in constituent concentration for upstream boundary conditions and for a point load such as a wastewater treatment plant, but these options were not implemented in the Truckee River study.

Benthic Algae Algorithm

The biomass of algae (or other primary producers) can play an important role in determining nutrient and dissolved gas concentration in rivers. As simulated in DSSAMt, the various modeled forms of nitrogen, phosphorus, dissolved oxygen, and carbon dioxide are assimilated or released in proportion to the biomass of algae present at a given point and time. The proportionality constants with respect to algal biomass for these metabolic products constitute yield coefficients. DSSAMt simulates the growth and accumulation of two forms of benthic algae, blue-green algae (Type 1; capable of fixing nitrogen) and non-blue green algae (Type 2; including diatoms and other forms such as Chlorophyta). Blue-green algae, generally the less abundant of the two types, tend to proliferate under conditions of low inorganic nitrogen and high temperature (> 25 C). In systems where endogenous (created within the system) primary production is important, significant errors in the simulation of dissolved oxygen and nutrients can occur if autotrophic biomass and related yield coefficients are improperly represented.

DSSAMt simulates the dynamics of the river periphyton by considering separately the blue-green and non blue-green algal assemblages. The benthic algae algorithm includes periphyton biomass as a state variable and three multi-variate rate vectors: primary production, algal respiration, and the removal processes that result in export of biomass from the system.

Primary Production

The periphyton models of McIntire (1973) and Runke (1986) provided the basis for DSSAMT's algal growth formulations. Algal growth is predicted by assuming that the community is in a state of balanced growth. Whenever nutrients or other environmental variables are suboptimal, growth becomes limited. A system diagram (Figure ?Hyper?) illustrates the connections between state variables (nutrients) and biomass. A maximum specific growth rate for each assemblage is estimated based on water temperature (Eppley 1972). The maximum rate is subsequently reduced according to the intensity of the suite of environmental "growth" variables to which the periphyton are exposed. The model considers the variables current velocity, intensity of photosynthetically active radiation, and the nutrients nitrogen and phosphorus. DSSAMt assumes Liebig's Law of the Minimum, which asserts that growth will be determined by whichever of the growth variables is shortest in supply relative to its optimum level. During a time step of a DSSAMT simulation normalized forms of these growth factors are used, along with a term that accounts for spatial limitation, to produce an adjusted rated that is applied to the photosynthesizing biomass.

The formulations used in DSSAMt for periphyton growth resemble those for phtyoplankton used in several other river water quality models (e.g., QUALIIe) except for the density dependent term (McIntire's "SPEC" or specific growth rate reduction factor) and the velocity enhancement term. The Monod model for uptake of the nutrients nitrogen and phosphorus is employed with values assumed for the Michaelis-Mentenconstants, which are used to adjust the maximum specific growth rate for the ambient temperature. A light-dependent growth rate reduction factor is estimated based on light transmitted through the water column. Thus far in the development and application of DSSAMT we have not had the need to treat phytoplankton as a functionally separate assemblage of algae since the rivers to which DSSAMT have been applied have not had significant populations of phytoplankters.

The growth rate for benthic algae for a given time period is a function of temperature, nutrients, light, current velocity, and biomass density. The maximum growth rate at the present temperature is first calculated. The maximum growth rate is then reduced by the limiting factor (either nutrient, current velocity, or light). The maximum growth rate for the given environmental conditions is next modified by the "SPEC" term. The SPEC term represents the growth rate reduction due to crowding. The maximum biomass is a function of numerous factors including type and size of bed material, current velocity, and algal species. The SPEC term is difficult to measure, but is very important. After the SPEC density is reached growth is reduced because there is no suitable substrate to colonize. Once the maximum biomass is reached, further biomass accumulation can occur only after a portion of the existing biomass decays and is removed by current velocity or grazers. The SPEC term is somewhat difficult to grasp conceptually since it represents the complex interaction of several variables; nevertheless, the use of SPEC or some similar formulation is essential to the realistic simulation of function in benthic ecosystems such as the Truckee River.

It is important to develop growth relationships for the river and seasons to be modeled. It is important to incorporate temperature growth relationships for the region to be modeled. The use of growth temperature relationships developed for warm southern rivers can underestimate growth during the cooler seasons. Light adaptation in the river should be explored. Use of literature values for the half saturation constant for light often underestimates growth under low light conditions in the fall and winter. Algae can adapt to low light conditions achieving relatively high growth rates under low light conditions.

Current velocity enhancement is an important factor in rivers which differentiates rivers from lakes. The flowing water brings a constant fresh supply of nutrients past the algae, so growth is possible at lower nutrient levels than in lakes. The flowing water also moves recycled nutrients downstream, unlike in lakes where nutrients are often lost to the sediments as phytoplankton fall to the bottom.

Respiration of Periphyton

The respiration rate of benthic algae is calculated as the sum of a temperature-dependentendogenous respiration term plus photorespiration. The respiration rate is temperature-dependent, and is calculated as a fraction of the photosynthetic rate. The respiration rates used in DSSAMt have been determined empirically for natural algal assemblages, although further studies are needed that deal with river periphyton.

Removal of Periphyton

Processes of removal of benthic periphyton in rivers are poorly understood and difficult to quantify. To estimate rates of removal of periphyton from the substratum DSSAMt includes the following processes: a) herbivory, b) mechanical disturbance by benthic organisms, and c) scour by the water current. Rate coefficients for these processes have been estimated using information from literature sources and parameter estimation during model calibration. Predicting the removal dynamics associated with periphyton is complicated by poorly quantified factors which include the growth form, age and condition of the algae, and the disturbance history of the benthos time (characteristics of, and time since last disturbance). Removal processes are critical to predicting the biomass and photosynthetic activity of periphyton. In rivers in which benthic processes significantly impact water quality, the degree of success with which removal is simulated may be the limiting factor on the overall performance of models to predict biomass levels, and hence dissolved oxygen and nutrients. Rate coefficients for scour by water current is especially important in determining the removal of biomass during periods of high flows. Such high flows are often referred to as flushing flows and are important, resetting the biomass to an earlier stage of community development. During extended periods without flushing flows biomass can accumulate resulting in lower than expected oxygen levels.

Algal biomass and its metabolic processes of production and respiration are linked to pH through the carbonate buffer system, directly to dissolved oxygen through respiratory consumption and photosynthetic production, and to phosphorus and nitrogen through nutrient uptake.

Macrophytes

Submerged vascular macrophytes are not included as a functional component in the present application of DSSAMt to the Truckee River. While not modeled explicitly, the functional role of macrophytes is included to the extent that a substantial amount of activity associated with submerged macrophytes consists of epiphytic growth of algae. Submerged macrophytes provide a physical substrate for proliferation of microphytes that respond to the processes of growth and removal simulated by DSSAMt. Although submerged higher plants could potentially have a significant effect on dissolved gas and nutrient flux, formulation and calibration of a numerical model for macrophytes would have required an effort far beyond the scope of the current study. The lack of macrophytes would become important when and where macrophytes would be able to grow and benthic algae would not be able to. This would be possible when nutrients such as phosphorus is limiting in the water column, but is available in the sediments for uptake by macrophytes. The other condition not included by using the benthic algae component to represent macrophytes, is when macrophytes growth occurs in calm pool sections of the river that under the current riffle pool construction would have little benthic algae present.

Nutrient Dynamics

Particulate and organically-bound forms of nitrogen and phosphorus are generally not available for direct uptake by river algae. DSSAMt simulates the transformations of such nutrient fractions (soluble organic nitrogen [SON], particulate organic nitrogen [PON], particulate phosphorus [PP], and soluble non-reactive phosphorus [SNRP]; see Figure 13-1) for several reasons. Each of these forms can ultimately undergo transformation in rivers to an inorganic ion (e.g., soluble reactive phosphorus [SRP] or ammonium [NH4N]) that is suitable for algal uptake. Such transformations constitute an important source of nitrogen and phosphorus for primary producers in some nutrient-limited riverine ecosystems. A second reason for including the organic and particulate fractions is that total nutrient loads transported from a modeled segment can only be simulated if all gross fractions are included in the model.

Numerical models of water quality constitute a trade-off between simplicity and complexity. Models that are excessively complex may require a database that is unavailable for calibration and verification. On the other hand, models that are too simplistic may not adequately incorporate relevant ecological processes necessary to address modeling objectives. The topic of nutrient dynamics provides an example of the need to match constituents and processes in a model with a suitable level of detail to simulate critical dynamics in the natural system. Un-ionized ammonia is a water quality issue of concern related to instream flow analyses because of fish toxicity. Un-ionizedammonia can potentially reach toxic levels when an ammonia load enters (or is produced within) a low-flow receiving stream under conditions of elevated temperature and pH. The elevated ammonia load can emanate from either allochthonous (generated outside of the system) sources or from decay of autochthonous (generated within the system) organic material. Accurate simulation of un-ionized ammonia concentrations as a function of flow requires the mechanistic representation of: I) processes of first order ammonia decay by means of nitrification, ii) algal uptake of ammonia, iii) preference of algae for uptake of ammonia compared to nitrate-nitrogen, iv) water temperature, and v) pH by means of the carbonate equilibrium. The absence (or inaccurate calibration) of any of these five factors can result in simulation results that either over- or under-estimate the concentration of un-ionized ammonia.

A second example of necessary detail in model formulation of nutrients that can significantly affect the accuracy of a water quality simulation relates to the rate of nutrient recycle and its effect on distribution of primary producer biomass, and in turn, dissolved oxygen. In DSSAMt a fraction of respired and removed nutrients (both nitrogen and phosphorus) is released downstream. Such a mechanism of release and transport downriver (termed nutrient spiraling) makes nutrients available for photosynthesis that had been tied up in biomass in upstream reaches. The potential for development of sub-standard dissolved oxygen conditions at low flows in a given reach of river could hinge on various factors (including the standing crop of biomass that had accumulated up to that time, the rate the biomass is respired and removed, and the rate of nutrient recycle). Although the recycle rate adds a degree of complexity to DSSAMt, without such a mechanism for nutrient spiraling the water quality simulations would lack a vital process characteristic of riverine ecosystems.

Differentiation of Metabolic Processes in DSSAMt Based on Fluvial Characteristics

Fluvial dynamics define two broad classes of habitat units that can be differentiated based on hydraulics. Habitat zones can be differentiated as either riffles, or pools as defined by base-flow characteristics. At low flows riffles tend to have shallow, rapid flow with a steep water surface gradient. Conditions present in riffles (cobble substratum, elevated velocity, shallow water depth, and increased exposure to solar radiation) tend to promote primary production by attached algae. Riffles are topographic high points in a river's longitudinal profile, which serve to dam the flow, thereby creating upstream pools that are relatively deep and slow-moving at base flow. Pools become depositional zones at low flow, and depending on the supply, sedimentary processes create conditions in the slow-moving water that tend to promote respiration of accumulated organic material. In DSSAMt algal growth rates and biomasses are calculated for the riffle conditions with the simulated biomass for algal assemblages representative of the optimal values found in the riffles. Uptake of nutrients and oxygen are calculated separately in riffle and pool habitats based on their fraction of the total area. For example if the relative area of riffles in the reach is 35% then uptake of a nutrient would be 0.35 of the amount based on the biomass in the riffle. Processes associated with sediment oxygen demand are assumed to occur only in pools.

The riffle-pool sequence defines a classification system for fluvial habitats that can be characterized based on criteria including gradient, velocity, depth and bed material (Rabeni and Jacobson, 1993). DSSAMt presumes that the photosynthetic and removal processes that act on algal biomass do so only in the fraction of reach that is riffle. The habitat classification scheme for Truckee River used for the present study (Figure hyper ), reflects the ratio of pool to riffles for different hydraulic sections of the river. In DSSAMt heterotrophic processes associated with sediment oxygen demand are assumed to occur exclusively in the fraction of each hydraulic reach characterized as pool.

The adjustment of depth and velocity in DSSAMt that allows modeled riffle conditions to be shallower and faster than the reach average constitutes a simple approach to addressing the matter of spatial heterogeneity in the modeled river ecosystem. Natural river channels possess a mosaic of habitat types that can be differentiated in part based on flow velocity and depth. The complexity of habitat patches varies with flow and longitudinal position on the Truckee River. In a general sense, habitat heterogeneity may be expected to be reduced at low flow and in the lower elevation (and gradient) reaches.

Data Requirements for DSSAMT Water Quality Model

In order to apply DSSAMt to a river system, coefficients and data specific to the river must be collected. This data then is used in the model to calibrate the model coefficients. At a minimum river specific data for the following must be collected:

Hydraulic coefficients

Boundary Conditions at headwater, point and diffuse inflows

Boundary values are required for all primary constituents listed in Table except temperature, benthic algae, and acidity. The boundary value for acidity is calculated using boundary value temperature, pH and alkalinity. Several options exist with respect to water temperature. In the simplest form water temperature and solar radiation can be entered on a daily or every five days. For this option the user needs to enter the maximum temperature, average temperature, time of maximum temperature, daily solar radiation and length of photoperiod. In the second option the user enters the data necessary for the model to calculate water temperature. The following meteorologic data are required

  • water temperature data at the headwater and for each tributary
  • Simulations with DSSAMt can be carried out with all or selected rate coefficients set to zero. This can be useful as a check on the flow balance and mixing assumptions. The model can be run just solely for temperature or other selected constituents.

    Simplifying Assumptions Used in DSSAMt

    1. sediment oxygen demand occurs in pools only
    2. processes of periphyton production and respiration occur in riffles only
    3. kinetic and hydraulic coefficients are uniform within pools and riffles for each river reach
    4. mixing is instantaneous and complete
    5. longitudinal dispersion is insignificant
    6. mean flow during a model pass adequately represents relevant conditions related to flow
    7. import of periphyton biomass from outside the system is negligible
    8. losses of periphyton from the system can be accounted for by endogenous respiration, invertebrate herbivory, and removal by water flow
    9. the phytoplankton and macrophyte communities are functionally insignificant to predictions of water quality or can be adequately represented by the benthic algae algorithm
    10. constant relationship between short wave radiation and PAR (photosynthetically active radiation if PAR values are not entered as boundary conditions

    References

    
    Hostetler, S.W. and L.V. Benson.  1992.  Meteorological and water-temperature data
    
         for Pyramid Lake, Nevada 1987-89.  U.S. Geological Survey, Open File Report
    
         92-159, Denver Colorado.  15 pp
    
    
    
    Nowlin, J.O.  1987.  Modeling nutrient and dissolved oxygen transport in the Truckee
    
         River and Canal downstream from Reno, Nevada.  U.S. Geological Survey
    
         Water-Resources investigations Report 87-4037.   Carson City, Nevada.
    
    
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    Last Modified March 28, 1996