Interpolating precipitation and its relation to runoff and non-point source pollution.

Area of Science:

• Hydrology and Environmental Science
• Spatial Data Analysis
• Water Resource Management

Background:

• Accurate rainfall data is essential for understanding hydrological processes and environmental impacts.
• Spatial variability in rainfall necessitates robust interpolation methods for accurate data.
• Existing interpolation methods have limitations, especially in complex terrain.

Purpose of the Study:

• To evaluate the effectiveness of different spatial interpolation methods for precipitation data.
• To identify the most suitable method for estimating rainfall in areas with strong spatial variation.
• To assess the impact of interpolation methods on runoff and non-point source pollution (NPSP) modeling.

Main Methods:

• Comparison of arithmetic average, Thiessen polygons, traditional inverse distance, and modified inverse distance methods.
• The modified inverse distance method incorporates elevation differences between stations and target areas.
• Application of interpolated rainfall data for WinVAST modeling of runoff and NPSP.

Main Results:

• Interpolation method choice significantly impacts accuracy when rainfall spatial variation is strong.
• The modified inverse distance method demonstrates superior performance in high-elevation areas with heavy rainfall.
• Random spatial rainfall variation leads to large estimation errors in runoff and NPSP, irrespective of the method.

Conclusions:

• The modified inverse distance method is highly effective for rainfall interpolation in complex terrain.
• Accurate rainfall input is critical for reliable runoff and NPSP predictions.
• The relationship between runoff error and pollutant concentration prediction can be unstable.