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A geospatial approach for estimating hydrological connectivity of impervious surfaces

•We present an approach for estimating hydrological connectivity of impervious areas.•Soils, rainfall, and relative area of disconnecting pervious control impervious connectivity.•Temporally varying parameters controls connectivity across low permeability soils.•Spatial flow path variability control...

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Bibliographic Details
Published in:Journal of hydrology (Amsterdam) 2020-12, Vol.591, p.125545, Article 125545
Main Authors: Sytsma, Anneliese, Bell, Colin, Eisenstein, William, Hogue, Terri, Kondolf, G. Mathias
Format: Article
Language:English
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Summary:•We present an approach for estimating hydrological connectivity of impervious areas.•Soils, rainfall, and relative area of disconnecting pervious control impervious connectivity.•Temporally varying parameters controls connectivity across low permeability soils.•Spatial flow path variability controls connectivity across highly permeable soils. Recent studies have reported that connected impervious areas – those impervious surfaces that contribute directly to runoff in a storm network or stream – are a better indicator of hydrologic response, stream alteration, and water quality than total impervious area. However, most methods for quantifying connected impervious areas require major assumptions regarding the definition of ‘connection’, potentially over-simplifying the role of variable climates, slope gradients, soils conditions, and heterogeneous flow paths on impervious surface connectivity. This study presents a new conceptual model and method for estimating hydrologically connected impervious areas (HCIA) that explicitly considers the effect of landscape and storm variability. The model separates impervious surfaces into two categories: directly or physically connected (Aphys) and variably connected (Avar) (impervious that drains to pervious). Of these categories, we investigated the sensitivity of Avar connectivity to varying soil conditions, slope gradients, rainfall properties, and hillslope geometry using PySWMM (a python interface for SWMM5). Simulations spanned a large parameter space with varying soil, slope, rainfall properties and geometries (i.e., relationships between the impervious and downslope pervious areas). PySWMM simulations were used to train and test a regression tree that predicts infiltration and connectivity of runoff from Avar surfaces, which provides excellent fidelity with PySWMM outcomes. To enable use of these methods in practice, we developed an ArcGIS tool that (1) delineates subcatchments; (2) extracts the impervious surface categories Aphys and Avar; (3) applies the regression tree algorithm to predict the fraction of incident rainfall that produces runoff across Avar; and (4) summarizes the resulting HCIA by subcatchment. Analysis of the regression feature importance shows that, in general, Avar connectivity is highly sensitive to the soil type, rainfall depth, area fraction, and antecedent soil moisture conditions of the downslope pervious area. We find that temporally varying parameters (e.g., rainfall and antecedent s
ISSN:0022-1694
1879-2707
DOI:10.1016/j.jhydrol.2020.125545