Design and Evaluation of Aquatic Ecosystems via 

Discrete Event Simulation 

Jorge Luis Romeu 

Research Professor 

Syracuse University, NY. Email: jlromeu@syr.edu 

Problem Statement 

Environmental issues will continue to be of great importance in the XXI Century. For they affect health, drive 

production costs up (short and long term) and are felt across borders, making them a global problem. In addition, 

ecological problems are fairly complex, involving many physical, human and socioeconomic factors that often 

interact with each other. Therefore, building tractable mathematical models for ecological problems is difficult and 

often leads to oversimplification of model assumptions. As a result, these mathematical models bear little 

resemblance with the real problem entities being modeled. 

In this paper we present an alternative analysis tool for environmental modeling. We propose the use of discrete 

event simulation, via specialized languages such as GPSS (General Purpose Simulation System). This approach 

presents several important advantages over purely mathematical ones, for environmental studies. For example, the 

modeler can now use broader distribution assumptions, in addition to Exponential or Normal. The modeler can 

now also consider all problem factors and their interactions. There is no longer need to keep the model formulation 

simple, in order to obtain a closed form solution. These two gains, by themselves, more than justify pursuing a 

simulation modeling approach. 

In the rest of this paper, we review the literature on this topic and present an illustrative prototype simulation 

model of an aquatic ecosystem. Our simulator has been successfully presented to audiences in the Environmental 

Protection Agency (EPA), the Great Lakes Research Consortium (GLRC) and the Mexican Institute of Water 

Technology (IMTA), among others. 

Literature Review 

From the above comments, it is apparent how simulation is an appropriate and useful tool in environmental studies 

and, therefore, should have been frequently used. However, as we will show in this section, it has seldom been 

applied in this area of research, and the question is “why not”? To try to answer this, we reviewed, among other 

sources, the last decade of the Winter Simulation Conference (WSC) an important forum for discrete event 

simulation. We found that (i) not many simulationists and operations research (O.R.) engineers have worked in 

modeling environmental problems and (ii) few environmentalists use simulation to model ecological problems. 

This is intriguing, since other quantitative researchers such as statisticians, are already actively applying their 

models and tools in ecological studies. 

Since 1990 we found few WSC papers dealing with environmental topics. In 1992, one WSC session was 

dedicated to natural resource models. Three papers were presented, dealing with ecological issues. Knisel et al. 

(1992) describe the development of a mathematical model with three basic components (hydrology, erosion and 

chemistry) to evaluate pollution from agricultural and forestry sources. The model simulates impact of 

management on response of field-size areas to climatic input. Wiles et al. (1992) conducted simulation 

experiments to evaluate scouting plans for use with weed control decision model for soybeans. As in the present 

paper, the authors were concerned with demonstrating the simulation methodology as a valid tool for evaluation of 

alternatives. Chang and Odeh (1992) presented a simulation model to generate monthly data for water quality 

using a first order Markovian process. 

Clymer (1993) in a limited review of the simulation literature discusses the merits "of discrete event model 

applications in the field of global simulation". Clymer discovers that "very little use has been made of discrete 

event models in world simulation." He suggests a list of eight global problems for discrete event simulation 

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(including land, fresh water, atmosphere and ocean physics and chemistry, including pollutants and thermal 

effects). And he presents a short bibliography of papers about modeling ecological problems or related subjects. 

In 1994, the WSC dedicated another session to waste management and environmental modeling problems and 

there were six papers presented. Boomer et al. (1994) develop a dynamic model that simulates expected activities 

occurring between underground waste storage tanks and define activities and functions for planned systems. Hand 

and Barr (1994) present two flexible simulation models for trans ceramic waste and perform trade-offs between 

different system design alternatives. Shaver (1994) describes a simulation model for the development of a 

proposed solid waste management system and evaluates alternative system configurations, throughput and 

capacity of proposed solutions. 

Hoefer et al. (1994) simulate a safety subsystem at an operating nuclear plant and compare different models on the 

basis of cost. Ross et al. (1994) simulate environmental restoration activities and remedial strategies for 

contaminated water and storage facilities, predicts system behavior, and compares alternative solutions. Lehr et al 

(1994) discuss the sensitivity analysis for a simulation model that investigates the oil weathering process. Ioannou 

(1999) uses simulation in dam construction, but only to optimize the use of construction equipment. Finally, only 

Romeu (1995 and 1997) briefly outlines a GPSS simulation model of an aquatic ecosystem, within several 

educational applications of simulation, and then proposes its use for applied environmental work. 

Modeling work done by ecologists, using simulation, is not much broader. For example, Hall et al. (1989) and 

Jourdonnais et al. (1990), among selected few, implement simulation models. Hall simulated (in FORTRAN) the 

Flathead River Basin, Montana, an ecological system including a lake and stream fish reserve. Jourdonnais 

showed how simulation provides a neutral context in which to negotiate the necessary trade-offs between 

economic and ecological advantages of a given resource management strategy, and arrive to a satisfactory 

solution. This was an extremely interesting characteristic of his model. 

Ecologists using simulation in their research, usually employ HOL languages (e.g. FORTRAN). The development 

of HOL programs can take several months and hundreds of lines of code (Hall, personal communication). This is 

another advantage of modeling ecosystems using simulation with a specialized language such as GPSS, where the 

development of these same programs take significantly less. 

Finally, some statisticians are already actively working in ecological problems. The American Statistical 

Association (ASA) has an Environmental Section that publishes a newsletter and offers yearly modeling and paper 

awards and several statistical institutes (e.g. at Penn State U.) specialize in this area and regularly publish their 

work in refereed journals (e.g. SPRUCE, 1995; Gilbert, 1987; Baltzer, 1996; CIMAT, 1993). 

The Simulation Modeling Approach 

Simulation modeling has been characterized, and rightly so, as a method of last resort. The first choice is always a 

theoretical model, when it exists. Unfortunately, it often doesn’t, or the required assumptions are so stringent that 

one is forced to oversimplify them in order to obtain a closed form solution. Secondly, we can turn to an empirical 

model, most often of the regression type. However, due to the many problem factors and their (first, second and 

higher order) interactions, the amount of data required to develop the model is beyond availability. In addition, the 

functional form selected for the regression model is usually arbitrary. Hence, we are left with the simulation 

approach as our “last resort tool”, since all other available modeling techniques are no longer applicable or are 

very difficult to implement. 

For implementing the simulation model we need (i) knowledge of, and relationships between, system components 

and (ii) data to estimate the distributions and parameters that describe their functions. Subject matter experts can 

provide the first. And the data for modeling and validation (the second requirement) can be obtained from the 

census as well as hydrological, industrial, business and agricultural databases of the local, state and federal 

governments. From the different system components we obtain the simulation model factors. From their interrelationships, we obtain the factor interactions. In the simulation model, these elements can be defined in minute 

detail, something more difficult (or even impossible) to do in the theoretical or regression models. 

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The use of special purpose simulation languages, such as GPSS, make the actual programming and debugging 

efforts less onerous than if HOL languages (e.g. FORTRAN, C) were to be used. Finally, the simulation model can 

yield a host of responses and performance measures of interest. Moreover, these model responses can then be 

recombined, via the use of adequate weights, into broader meta-responses that allow a more objective comparison 

of the conflicting and competing interests of the various aquatic ecosystem constituents (e.g. ecologists vs. 

business developers). 

Once the simulation program is written and verified (programming part is correct) it needs to be validated. That is, 

we need to show that the simulation actually mimics the real system it purports to model. When modeling existing 

aquatic ecosystems, the real data also exists. Hence, we can use it for validation (to compare it with the simulation 

model output). Once the validity of the model is established, we can start experimenting with it, which is the final 

objective of the entire simulation exercise. 

Simulation is a numerical procedure. Hence, running it once and obtaining a point estimator is not very useful. We 

want to run a series of similar cases (a sample of pseudo-independent and identically distributed observations) and 

use them to derive confidence intervals (CI) for the system responses or performance measures of interest. We can 

also implement what-if analyses and hypotheses tests for specific system conjectures of interest. In addition, we 

can investigate whether some specific mode factor, or a combination of them, effect the model response and, if so, 

to estimate the magnitude and direction of these effects. Such results are obtained via the implementation of 

statistical experiments, usually factorial or (if many effects are under scrutiny) fractional factorial designs. To 

illustrate this approach we present, in the next section, a simulation model for an aquatic ecosystem. 

An Applications Example 

We describe below the prototype simulation model of an aquatic ecosystem that is common along the coast of the 

Gulf of Mexico. It is composed of estuaries, wetlands, lagoons and river-lake systems. Two specific examples of 

these ecosystems are the river ports of Tampico and the Papaluapan-Alvarado estuary, both in Mexico. Such 

ecosystems are very important for the preservation of species and their habitats (wetlands, lagoons) as well as for 

their socioeconomic development (river commerce, industry, agriculture, urban uses, energy and recreation). But 

these two activities define two constituencies (environmentalists and business developers) that often compete for, 

and disagree about, the ecosystem resources and how to better use them. And there is a need for an analysis tool 

(the simulation model) to help them better resolve their differences and work together. 

We built a GPSS simulation model of a system of two inter-connected water environments: the Panuco river and 

its lagoon (the river port of Tampico, Tamaulipas, in northeastern Mexico). These water environments (ecosites) 

constitute our real ecosystem. In our simulation model, each ecosite has its own parameters: maximum and 

minimum water capacity levels, physical configuration and water distribution policies (Figure 1). These ecosite 

parameters reflect the socioeconomic and political requirements, constraints and characteristics of the two abovementioned constituencies. The simulation model assesses the overall effects that constituent-driven changes in the 

ecosystem factors, produce in the operation levels and the services rendered by the ecosystem (e.g. port activities, 

agricultural yields, urban quality of life). The simulation model also assesses the corresponding environmental 

effects (e.g. deterioration of wildlife habitats). 

Figure 1 shows a schematic of the above-described aquatic ecosystems, which has several deterministic and 

stochastic water delivery obligations. The lagoon provides water for drinking and other personal uses to the 

population, as well as for agriculture, animal husbandry, etc. It also provides humid environments (wetlands) for 

wildlife reproduction as well as recreational facilities for the population. The river port provides the means of 

transportation to bring in raw materials and food, and to export regional products. Its derived economic activity 

supports agriculture, local industry, commerce and general transportation needs of the population, therefore 

generating badly needed jobs. In our model we do not consider the effects of evaporation or possible water theft 

which, if need arises, can also be incorporated into the simulation. All the above-described activities and services

constitute outputs of our aquatic ecosystem model. 

On the other hand, water for all system obligations come from only three ecosystem sources: rain, the water table 

and “borrowing” from the other water mass, either lagoon or the river port itself. Let’s explain. 

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Pump

Max Max

Min

Min

River 

Port Lagoon

Water Table

Figure 1: Schematic of the River Port and Lagoon aquatic ecosystem. 

Figure 2: Complete Factorial Experiment for the Simulation

1096 688

Rain

Seasonality

636 502

Dry

1146 744 Response: 

Total Cost A/2

Water 

Transfer 

Policy

680 581 A/3

One Two

River Port Capacity

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When the rainfall (which is a stochastic input, whose statistical distribution is specified in the simulation model) is 

insufficient, the lagoon or the river levels drop below acceptable levels (Min, in Figure 1). Then, the other ecosite 

(river or lagoon) can “lend” some of its water via a water transfer. Alternatively, and as a last resource, ecosites 

can also obtain water from the water table. However, there is a high economic and environmental cost associated 

with water pumped from the water table. All the aquatic ecosystem ecological and economic variables or factors 

defined in Figure 1, and their trade-offs, are summarized in Table 1. 

The river port capacity and configuration design (Max, in Figure 1) depends on the available water and the 

commerce needs. If there were enough water, the river canal could be widened to accommodate more ships, 

enhancing its commercial capacity. But as water falters during the dry season, the river level drops below 

acceptable levels (Min) and ships cannot operate unless additional water is transferred from the lagoon or the 

water table to fill the gap. On the other hand, a narrower river canal would ensure the uninterrupted port operation 

at lower water levels, without need for drawing excessive water from the lagoon, or draining the water table. But 

this would also let fewer ships access the river port, inducing a slower economic activity. 

The total system operation cost, as well as the costs of distributing water among competing user constituents 

(agriculture, industry, commerce, general population) constitute the economic objective, or response function of 

interest. We include the corresponding linear, non-linear, deterministic and stochastic problem constraints (Max, 

Min, rainfall distribution, etc). The simulation model can also consider other performance measures such as the 

number of water transfers between ecosites, the number of (and time to first) water stock-outs, changes in 

agricultural, industrial and commercial outputs, in levels of employment, etc. all of which are a direct consequence 

of the levels of water available in the two ecosites (or water masses). Other problem constraints include operating 

budgets, available land, ecological mandates, water distribution priorities, etc. 

The response variable used in our example was the total cost of water transfers. This included, in addition to the 

cost of physically transferring water from one ecosite to the other, also the indirect costs induced by the ensuing 

lack of water (affecting the economy, ecology, population lifestyle, etc). Other ecological responses (e.g. size of 

the wetland areas, changes in flora and fauna reproduction levels) may also be used. The possibility of trading off 

real life conflicting (economic vs. ecological) management policies (e.g. rationing versus selling water) adds 

another important feature to the simulation modeling capabilities. 

Implementation 

Our GPSS simulation model was used to assess and compare the economic and ecological effects of diverse water 

management strategies and river and canal designs, on the entire ecosystem. The simulation was also used to 

assess the joint impact of these strategies and ecosite designs on the ecological, social and economic resources of 

the entire ecosystem. 

In particular, we wanted to assess the joint effect of three factors. One, was the use of the water from the water 

table versus the use of several inter-ecosite water transfer policies, to balance water deficits within the ecosystem. 

This factor is important because the water table depletion produces serious and long-term adverse effects on the 

flora and fauna, as well as on agriculture and animal husbandry. In addition, the amount and speed of the ensuing 

water transfers affect water temperature and salinity, which, in turn, impacts fish and foul reproduction. 

Another factor of interest was the ecosite configuration and design. Port and canal design configurations (e.g. their 

sizes and capacities) impact the available water in an ecosystem. The size of the river port (capacity for servicing 

one or two ships) determines the level of commerce, thus impacting the number of jobs in industry, agriculture, 

etc. Therefore, for economic reasons, we want the larger port. On the other hand, maintaining such larger capacity 

during the dry season requires draining water from the lagoon and perhaps even from the water table. This, in turn, 

affects agriculture and the environment. Lagoon water draining reduces crop yields and levels of species 

reproduction (smaller wetlands). In addition, tampering with the water table may produce irreversible damage to 

the ecosystem. Therefore, for ecological reasons, we want a small port. This is a classical example of two different 

constituencies that conflict, due to requiring the use of the same, constrained (water) resources, while espousing 

very different value systems. 

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 Ecologic

 Table 1 : Aquatic Ecosystem Trade-Off Design/Decision Variables and Costs 

Controlled Variables

 Economic

* EcoSite Replenishing Levels (MIN) * Wetland size 

* EcoSite Capacities (MAX) * Water Temp 

* EcoSite Ordering Schedule * Transfer Speed 

* Water Transfer Policy * Water Salinity 

* Water Usage Policy * Pollution 

* Water Shortage Policy * Area/Depth 

* Water Table Level * Fish/Foul Population 

* EcoSite Initial Conditions * Fish/Foul Reproduction 

Associated Costs:

* of pumping water from Water Table * of foul/fish extinction 

* of pumping water from EcoSite to EcoSite * of biodegradation 

* of sale of water to users * of habitat Damage 

* of water shortages * of habitat clean up 

* of lack of water * of habitat reconditioning 

* indirect costs * of species relocalization

* total costs * total costs 

Uncontrolled Variables:

* Water Table capacity * Salinity 

* Weather conditions * Oxygen 

* Rain schedules * Temperature 

* Water Theft * Evaporation 

Types of Problems:

* New system design * Regulation trade-off 

* Existing system optimization * Impact evaluation 

* Comparison of strategies * Conflict mediation 

* Education of the population * Risk Analysis 

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The third factor of interest, seasonality, depends entirely on nature and mainly serves to complicate the abovedescribed situation by its interaction with the first two factors. When rain is scarce and the port design is too large, 

there is a water shortage. Maintaining the port open during the dry season then requires draining the water table 

or the lagoon, thus endangering the health of the ecosystem as well as the agriculture and animal husbandry 

activities. And there is also less water to serve the general needs of the population (drink, hygiene, etc.). 

Thus, the three factors included in our statistical model (and their levels) were the following. First, River Port 

capacity (denoted α), which had two levels: high (two ships) or low (only one). The second factor was Seasonality 

(denoted β) with two levels, defined by the rain schedules for the rainy and dry seasons. The third factor (denoted 

γ) was the (inter-site) Water Transfer Policy. The first priority was always pumping from the water remaining 

above the alternative ecosite respective minimum level (transfers were 1/3 or 1⁄2 of such availability). Only when 

the alternative ecosite did not have enough water, was it allowed to pump water from the water table. The factor 

interactions were denoted with double letters (e.g. αβ). Each factor had two levels, denoted with letters i=j=k=2. 

Finally, we used l=9 replications for each run setting or factor combination. 

The mathematical expression of the (23 complete) factorial model implemented is: 

Yijkl = μ+αi+βj+γk+(αβ)ij+(αγ)ik+(β)jk+(αγ)ijk+εijkl (1) 

The response variable (denoted Y) was Total Operation Cost, considered in the following manner. The port 

capacity (one or two ships) influenced, through the amounts of goods moved in and out of the region, the levels of 

employment in agriculture, industry and commerce. On the other hand, capacity also determined the water 

shortage levels. Water transfer policies defined the amounts (and sources) of water "loaned" from one ecosite to 

the other, during a water shortage. Smaller amounts implied more frequent transfers -but allowed more water for 

use in its original ecosite, e.g. transportation, wetlands or agriculture. On the other hand, larger but less frequent 

transfers decreased the levels of the lagoon and river port, also effecting the transportation and agriculture as well 

as the wetland sizes. These, in turn, affected employment and species reproduction levels. Finally, seasonality 

(rainy and dry seasons) aggravated the above-mentioned situations, directly as well as via its interaction with the 

two other factors. 

Therefore, the response function Y included the cost of the actual water transfers as well as the social, economic

and ecologic “costs” that such transfers induced. Individual costs were defined by placing a dollar value on their 

impact on each of the aforementioned elements, by unit of water level lost, in each of the two ecosites. In our 

simulation, costs were defined by the “business developer”. Similarly, experiments using our model but with costs 

defined by “ecologists” or other interested parties can also be run and their results can be compared. This is one 

more advantage of simulation in these types of studies. 

Analysis results 

The GPSS simulation model was run for a 23 complete factorial experiment. A batch means output analysis 

method was used. Each batch consisted of one month of daily operation, starting at “average level”. The first ten 

batches were discarded to account for the transient or warm up period. After this, we took every fifth batch, to 

avoid inter-batch correlation, for a total of nine replications. The analysis of variance results (Table 2) show how 

every main effect (as well as the interaction between ecosite capacity and water transfer policy) were all 

statistically significant. The other interactions were not statistically significant. 

 Source D. F. Mean Square F Value P-Value 

River Port Capacity 1 1219401 588.71 0.000 

Water Transfer Policy 1 1828892 882.96 0.000 

Seasonality (Rain/Dry) 1 58186 28.09 0.000 

Enviro-Site x Policy 1 373104 180.33 0.000 

Table 2: Analysis of Variance Table for the Simulation Experiment 

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The ecosite capacity factor (design of the river port) was highly significant. The amount of water (and hence of 

transfers) required to maintain the river port open (as well as adequate wetlands for reproduction needs) depend on 

the river port size. But river port capacity also affects the need for water transfers, whose costs include the 

(environmental and economic) effects produced by such transfers. The statistical analysis showed that the single 

most significant factor was Water Transfer Policy. Assessing water transfer sizes and their induced costs was an 

important analysis objective. Assessing the effects of establishing different water prices versus establishing water 

distribution policies (i.e. rationing) is now possible with the simulation model. 

Another important problem element, that can now be analyzed, is water user profile (e.g. different agricultural, 

industrial, commercial and household water requirements). Water usage impacts directly and indirectly the quality 

of the life of both, the population and the ecosystem. It produces low level, short-term, effects (e.g. lack of water 

for lawns, for swimming pools). It can also reduce the size of wetlands for wildlife reproduction, for habitats, etc. 

inducing long-term effects. Stochastic user profiles can be included in the simulation model, something much 

more difficult to implement either in the theoretical or empirical models. Simulation can also consider the joint 

effect of several of these factors (Table 1), via complete and fractional factorial experimental designs, obtaining 

through them, valuable quantitative information. 

We can see in Table 3 how the two largest changes in the Cost response function (in respectively inverse direction) 

correspond to factors Size of River Port and Water Transfer Policy. These factors modify the total cost (response) 

in Equation (1) by the amount and the direction indicated Table 3, (adding or subtracting from average cost). 

Factor Change Effected 95% CI Factor Effect on Response 

Size of the river port One to two ships capacity Cost decrease in $232 to $287 

Water Transfer Policy Transfer 1/3 to 1/2 of availability Cost increase in $293 to $344 

Seasonality Dry into Rainy Season Cost decrease in $33 to $80 

Size x Transfer Policy Dry/one ship to Rain/Two ships Cost decreases in $122 to $165 

Table 3: Examples of model-derived quantitative information (CI of changes in the Total Cost function) 

Table 3 is based on Figure 2, a schematic of the 23 complete factorial design implemented. For example, the most 

expensive (total cost) factor combination ($1146) corresponds to operating a port designed for one ship, during dry 

season and with 1/2 availability water transfer policy. And the least expensive combination ($502) corresponds to 

operating a port designed for two ships, during rainy season and with a transfer policy of 1/3 of ecosite available 

water. The better of the two water transfer policies analyzed is moving only one-third of the available water in the 

ecosite. Under such policy, there is not a very large difference between one or two ships port design –but two ships 

is less expensive. 

Again, the simulation results presented are based upon cost “values” defined by the “business developers”. The 

simulation can also be run with different cost values (of agricultural production losses, wetland deterioration, 

species reproduction reduction, etc.) defined by other constituencies (ecologists, farmers, etc.). Such other cost 

definition may yield totally different results. We can also run alternative “what if” scenarios. These can then be 

traded-off, compared, etc. The simulation now becomes a decision-support and an arbitration tool. 

For example, multi-criteria (ecological, social, economic, etc.) system responses (from the elements in Table 1) 

can now be obtained, by combining (say k) contrasting and competing individual responses (say Y ) into a 

single, complex one. This is done by defining a set of weights (say 

Yk, ,

1 K

1; ≤ ≤ ;∑ = 1 i i α i k α ) that respond to the 

different priorities or costs defined by the different constituencies interested in the ecosystem problem. The linear 

combinations formed using such weights (∑α Yii

)quantify the contrasting policies and philosophies of these 

different constituencies. This way, a more objective comparison of such competing and contrasting policies, 

produced by the (unbiased) simulation model results, can now provide a more rational environment where these 

diverse constituencies can discuss their differences and better reach a consensus. 

Page 193
Summary and Conclusions 

This paper discussed how discrete event simulation can be effectively used to model environmental problems and 

shows the many advantages that simulation brings to the environmental modeling area. These two key advantages 

are (1) the possibility of the consideration and inclusion, in the simulation model, of all the problem 

characteristics, and (2) the possibility of doing so without having to resort to an oversimplification of the problem 

or its assumptions in the model. 

Finally, we implemented a prototype simulation model and analyzed an aquatic ecosystem with it. We discussed 

the modeling requirements and constraints, we developed a full factorial experiment as an illustrative application, 

where the entire approach was implemented, we presented different uses of objective functions that can be built 

and contrasted, and we provided examples of how the simulation model can now be used as an arbitration tool, in 

addition to an analysis and design tool, to help the different constituencies arrive to a consensus. 

With this illustrative example, we hope to provide an answer to the question in our initial literature search, 

regarding why simulation is not more widely used in this area. And we also hope to encourage the practical uses of 

discrete event simulation in the area of ecological modeling and analysis. 

Page 194
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