The improved analytical stochastic model of infiltration trenches for stormwater quantity control

As one of the infiltration-based low-impact development (LID) measures, infiltration trenches are widely used to reduce runoff and improve water quality. The conventional analytical stochastic approach developed for use in the hydrologic design of infiltration trenches often overestimates the trench's runoff reduction performance when the infiltration rate at the bottom of the trench exceeds some high level or when the size of the trench is smaller than some threshold level. Furthermore, the appropriateness of using kernel density estimation (KDE) for rainfall event separation and frequency analysis has not been examined yet in the actual hydrologic design of LIDs. To overcome these deficiencies, an improved analytical stochastic model (ASM) was developed in this study incorporating the KDE-based rainfall event characterization and a modified formula for estimating the effective storage capacity of trenches. The calibration, verification and application of the improved ASM were systematically presented and their results were discussed. The accuracy of the improved ASM were verified by comparing the analytical results against the corresponding continuous simulation results. A large number of design cases in nine provincial capital cities of China were analyzed using the improved ASM and considering the effects of soil types, trench's storage reservoir depth, area ratio, and climate conditions. The improved ASM of infiltration trenches is useful for quickly and accurately assessing their water quantity control performances. The results indicated that the accuracy of improved ASM improved by up to 71 % in terms of R-square among the 9 study areas compared to conventional ASM. The improved ASM can be used to directly and quickly calculate the useful hydrologic performance indices for a given trench size, soil condition, area ratio and local climate condition, it can thus provide scientific guidance for the Sponge City construction in China and sustainable urban stormwater management.