TY - JOUR
T1 - Modeling of Subsurface Throughflow in Urban Pervious Areas
AU - Nielsen, Kristoffer T.
AU - Nielsen, Jesper E.
AU - Uggerby, Mads
AU - Rasmussen, Michael R.
N1 - Funding Information:
This research was partially funded by The Foundation for Development of Technology in the Danish Water Sector, the Innovation Fund Denmark, Aarhus Vand, EnviDan A/S, and Aalborg University.
Publisher Copyright:
© 2020 This work is made available under the terms of the Creative Commons Attribution 4.0 International license,.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - Infiltration excess runoff, i.e., runoff as a result of the rainfall intensity exceeding the infiltration capacity of the soil surface, has traditionally been considered the only contributor to the surface runoff from urban pervious areas. However, recent studies show that subsurface throughflow also can be a significant contributor to urban stormwater runoff. Although rainfall-runoff from urban pervious areas can contribute with large quantities of runoff, only little knowledge exists on this topic. In this study, experimental field observations of subsurface throughflow from the literature are used to assess the capability of different models to simulate this type of runoff. It is investigated how well three new modeling approaches in urban drainage engineering (linear reservoir, regression, and shallow neural network models) performs in simulating subsurface throughflow compared to two commonly used models (the time-area and kinematic wave model). The models are compared with the measured runoff rate and evaluated by the root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and Bayesian likelihood (L). Generally, a neural network containing 60 neurons and using up to 180 min of data back in time produces the best results (RMSE=0.59 Lmin-1, NSE=0.91, and L=0.92). However, both the kinematic wave (RMSE=1.06 L min-1, NSE=0.71, and L=0.76) and linear reservoir model (RMSE=0.98 L min-1, NSE=0.75, and L=0.78) generate reasonable results despite their significantly simpler modeling approaches.
AB - Infiltration excess runoff, i.e., runoff as a result of the rainfall intensity exceeding the infiltration capacity of the soil surface, has traditionally been considered the only contributor to the surface runoff from urban pervious areas. However, recent studies show that subsurface throughflow also can be a significant contributor to urban stormwater runoff. Although rainfall-runoff from urban pervious areas can contribute with large quantities of runoff, only little knowledge exists on this topic. In this study, experimental field observations of subsurface throughflow from the literature are used to assess the capability of different models to simulate this type of runoff. It is investigated how well three new modeling approaches in urban drainage engineering (linear reservoir, regression, and shallow neural network models) performs in simulating subsurface throughflow compared to two commonly used models (the time-area and kinematic wave model). The models are compared with the measured runoff rate and evaluated by the root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and Bayesian likelihood (L). Generally, a neural network containing 60 neurons and using up to 180 min of data back in time produces the best results (RMSE=0.59 Lmin-1, NSE=0.91, and L=0.92). However, both the kinematic wave (RMSE=1.06 L min-1, NSE=0.71, and L=0.76) and linear reservoir model (RMSE=0.98 L min-1, NSE=0.75, and L=0.78) generate reasonable results despite their significantly simpler modeling approaches.
KW - Linear reservoir model Kinematic wave
KW - Neural network
KW - Pervious surfaces
KW - Stormwater runoff
KW - Subsurface throughflow
KW - Urban drainage
KW - Linear reservoir model Kinematic wave
KW - Neural network
KW - Pervious surfaces
KW - Stormwater runoff
KW - Subsurface throughflow
KW - Urban drainage
UR - http://www.scopus.com/inward/record.url?scp=85091480135&partnerID=8YFLogxK
U2 - 10.1061/(ASCE)HE.1943-5584.0001990
DO - 10.1061/(ASCE)HE.1943-5584.0001990
M3 - Journal article
AN - SCOPUS:85091480135
SN - 1084-0699
VL - 25
JO - Journal of Hydrologic Engineering
JF - Journal of Hydrologic Engineering
IS - 12
M1 - 04020050
ER -