Abstract
Quantification of spatial and temporal patterns of rainfall is an important step toward developing regional water sewage models, the intensity and spatial distribution of rainfall can affect the magnitude and duration of water sewage. However, this input is subject to uncertainty, mainly as a result of the interpolation method and stochastic error due to the random nature of rainfall. In this study, we analyze some rainfall series from 30 rain gauges located in the Great Lyon area, including annual, month, day and intensity of 6mins, aiming at improving the understanding of the major sources of variation and uncertainty in small scale rainfall in-terpolation in different input series. The main results show the model and the parameter of Kriging should be different for the different rainfall series, even if in the same research area. To the small region with high den-sity of rain gauges (15km2), the Kriging method superiority is not obvious, IDW and the spline interpolation result maybe can be better. The different methods will be suitable for the different research series, and it must be determined by the data series distribution.
Highlights
Precipitation is in many cases the most important input factor in hydrological modeling [1]
This paper focuses on three interpolation methods for the different rainfall series
The Inverse Distance Weighted (IDW) and Spline methods are referred to as deterministic interpolation methods because they assign values to locations based on the surrounding measured values and on specified mathematical formulas that determine the smoothness of the resulting surface
Summary
Precipitation is in many cases the most important input factor in hydrological modeling [1]. The role of rainfall is essential for urban hydrology: it is the driving phenomenon of runoff mechanisms, in an urban context. Its variability constitutes a significant source of uncertainty for hydrological modeling. Assessing rainfall variability is an important element to developing conceptual and predictive models of runoff, pollutant loading, and river dynamics. Quantification of spatial and temporal patterns of rainfall is an important step toward developing regional water sewage models. The intensity and spatial distribution of rainfall can affect the magnitude and duration of pollutant washoff to the ocean [2,3], which are an input for hydrological models. Urban hydrology requires rainfall measurements with high temporal and spatial resolution
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