Introduction to L-THIA Low Impact Development
Low Impact Development (LID) practices aim to reduce the impacts of stormwater and
pollutants from land development. The goal of LID is to maintain, as closely as
possible, the predevelopment hydrologic regime for new developments or move
toward the original hydrologic regime in existing urban areas.
L-THIA/LID is an easy to use screening tool that evaluates the benefits of LID
practices. The Long-Term Hydrologic Impact Assessment (L-THIA) model estimates
the average annual runoff and pollutant loads for land use configurations based
on more than 30 years of daily precipitation data, soils, and land use data for
an area. In this model of the L-THIA, users need only to input the follow:
- location (state and
- type of soil in the area
where the land use change is to occur (available
online if unknown)
- type and size of land
use change that will occur (e.g., 100 acres
of agricultural land converted to 50 acres high-density
residential and 50 acres commercial).
- LID practice(s) to screen.
The L-THIA/LID model consists of two screening levels for the LID approach.
Basic screening allows the users to adjust the percent of imperviousness for
particular landuses. Lot-level screening consists of a suite of LID practices
such as bio-retention (rain gardens), porous pavement, narrowing impervious
surfaces (streets, sidewalks and driveways) and vegetated rooftops. These
practices intercept, redirect, and slow the movement of runoff and pollutants
moving through a watershed.
L-THIA/LID will generate estimated runoff volumes, depths, and expected nonpoint
source pollution loadings to waterbodies, based on the information provided by
the user. Results can be displayed in tables, bar graphs, and pie charts.
We provide a short training walkthrough, an L-THIA LID tutorial training document that includes 3 Tutorials and a design manual, and a literature review.
Science Behind L-THIA (7 minutes)
Long Version - L-thia background (18 minutes)
First Steps - set up the model
Basic Screening Model(no BMPs)
Lot-level Screening (with BMPs)
L-thia Results Table