Urban Water Storage Capacity Inferred From Observed Evapotranspiration Recession

Abstract

Water storage plays an important role in mitigating heat and flooding in urban areas. Assessment of the water storage capacity of cities remains challenging due to the inherent heterogeneity of the urban surface. Traditionally, effective storage has been estimated from runoff. Here, we present a novel approach to estimate effective water storage capacity from recession rates of observed evaporation during precipitation-free periods. We test this approach for cities at neighborhood scale with eddy-covariance based latent heat flux observations from 14 contrasting sites with different local climate zones, vegetation cover and characteristics, and climates. Based on analysis of 583 drydowns, we find storage capacities to vary between 1.3 and 28.4 mm, corresponding to e-folding timescales of 1.8-20.1 days. This makes the urban storage capacity at least five times smaller than all the observed values for natural ecosystems, reflecting an evaporation regime characterized by extreme water limitation.

Keywords: recession analysis; urban climate.

Figure 1

Illustration of the recession analysis. 24‐hour aggregated evapotranspiration versus the number of days following the last hour of precipitation for an example drydown from the Seoul data set with the fitted recession curve. Note that the fit was obtained by a linear fit on log‐transformed data (see Data and Methods). In the figure the parameters are indicated.

Figure 2

Daily average evapotranspiration versus the day since the last precipitation with in red (continuous) the recession curve using the median parameter values, in blue (dotted) the 5th and 95th percentile of the median distribution from the bootstrapping re‐samples, and in gray all individual drydowns. The boxplots show the spread of the observations. The parameters of the fitted curves are shown in Table 1. Since the parameters are based on individual drydowns, they do not necessarily follow the trend of the distributions.

Figure 3

The seasonal dependency of the median S ₀ for the sites on the northern hemisphere (Melbourne is included shifted by half a year) in blue and for Singapore as gray dots. The uncertainty is determined similarly as in Figure 2.