Page 192 - Biosystems Engineering

GIS-Based W atershed Modeling Systems 171

variety of purposes, including design of soil conservation practices, water
table management, prevention of chemical pollution of surface bod-
ies of water and groundwater, protection of aquatic biota, and devel-
opment of TMDLs. In the years ahead, worldwide, watershed models
will play an increasing important role in managing NPS pollutants.

5.2.2 Origin of Watershed Models
Because NPS pollutant transport is mainly driven by meteorological
events, the early to mid-20th century saw the development of math-
ematical descriptions of individual hydrologic components [e.g.,
infiltration, runoff, evapotranspiration (ET), and interception]. The
digital revolution of 1960s witnessed the integration of individual
hydrologic components (Singh and Woolhiser 2002) into functional
models that can be applied at various spatial and temporal scales.
Initially, the models were developed and applied at point and field
scales. However, it was quickly realized that, to truly address NPS
pollution, watershed-scale models are needed. The development of
watershed-scale hydrologic and NPS models in the United States
began in response to the CWA (Arnold and Fohrer 2005). Examples of
these models include the Agricultural Non-Point Source Pollution
Model (AGNPS) (Young et al. 1987), Annualized AGNPS (AnnAGNPS)
(Bingner and Theurer 2003), Hydrologic Simulation Program—Fortran
(HSPF) (Bicknell et al. 2001), the Kinematic Erosion Model (KINEROS)
(Woolhiser et al. 1990), and the Soil and Water Assessment Tool (SWAT)
(Neitsch et al. 2002).

5.2.3 Characterization of Watershed Models
Models in general and watershed models in particular can be charac-
terized as mechanistic or empirical (based on the cognitive value of a
model), stochastic or deterministic (based on the character of results
obtained), linear or nonlinear (based on the mathematical properties
of the operator function), event or continuous simulation, and lumped
or distributed parameter model (Haan et al. 1982).
Truly mechanistic models are those in which governing physical,
chemical, and biological laws and the model structure are well known
and can be described by mathematical equations. Empirical models
are used when model structure and governing laws are unknown or
the mechanistic model is so complicated that simplification of model
behavior is needed. In reality, most current watershed-scale models
have mechanistic and empirical components. Further, most make an
attempt to model physical, chemical, and biological processes that
occur on land or in bodies of water (e.g., streams, ponds, lakes, or
reservoirs). These models are best described as process-based mod-
els. If any of the variables in a process-based model is regarded as a
random variable having a probability distribution function, the model
is called a stochastic model. However, if all of the variables are free
from random variations, then the model is a deterministic model.