Affordable Access

Publisher Website

Watershed Models

Elsevier B.V.
DOI: 10.1016/b978-008045405-4.00240-8
  • Agent-Based Models
  • Canonical Correspondence Analysis
  • Diffuse Pollution Modeling
  • Ecological Watershed Models
  • Erosion Modeling
  • Mechanistic Models
  • Rainfall–Runoff Transformation
  • Self-Organizing Maps
  • Soil Pollution Modeling
  • Stochastic Models
  • Water Quality Models
  • Chemistry
  • Computer Science
  • Ecology
  • Mathematics


Numerous watershed models have been developed in silico (computer) or physically built by scientists over the last 50 years. The category ‘ecological watershed modeling’ includes mostly mathematical computer models. The concepts of the first computer model, the Stanford Watershed Model, are still valid. Watershed models describe complex interactions of various terrestrial components during and between the rainfall/snowfall and other inputs. Other examples of input parameters and factors, besides precipitation, include wet and dry atmospheric deposition, impact of diffuse pollution, chemicals in fertilizers and pesticides, and emissions and impact of traffic. This article describes primarily computer modeling of terrestrial processes that affect water quality and biotic integrity of aquatic systems. These models can be separated into two distinct categories: (1) deterministic, also called mechanistic, models; and (2) indeterministic models that include probabilistic models and artificial intelligence, for example, artificial neural net (ANN) or genetic algorithm models. The models can be also categorized ‘lumped parameter and distributed parameter’. Lumped parameter models can be both stochastic and deterministic. Distributed parameter models are mostly deterministic. New developments in the twenty-first century such as applications of hybrid combinations of deterministic models with agent-based modeling or models developed by robust ANN knowledge extraction from large databases are also described.

There are no comments yet on this publication. Be the first to share your thoughts.


Seen <100 times