Statistical Attribution of the Influence of Urban and Tree Cover Change on Streamflow: A Comparison of Large Sample Statistical Approaches

Abstract

Shifts in climate characteristics, such as precipitation totals, phase, and timing are widely considered to be the dominant drivers of hydrologic change, but land cover changes, consisting of changes in the biological or physical features present in a landscape (e.g., forested or urban area), also have the potential to drive changes in streamflow, potentially even offsetting the influence of climate (Slater, Anderson, et al., 2021). The effects of land cover changes on hydrological extremes, such as worsening flood risk (e.g., Bradshaw et al., 2007; van Dijk et al., 2009), or as potential mechanisms by which hydrological and climatic risks may be managed or offset (e.g., Dadson et al., 2017;Dixon et al., 2016) remain poorly understood. It is clear that land cover changes can alter hydrological response to precipitation events by influencing the degree and rate at which water is intercepted and evaporated, stored, or allowed to run off into a river channel (Filoso et al., 2017;Jacobson, 2011;Shuster et al., 2005), however, the extent to which they do so lacks clear definition.There are a number of ways in which land cover change might be expected to influence the magnitude of high, mean, and low daily streamflows. Widely discussed in the literature, urbanization is typically expected to increase high flows and flood risk (

Contribution of urbanisation to non-stationary river flow in the UK

“…The greater contribution of urbanisation to low flow may also be due to the development of sewer systems and hard drainages associated with urbanisation, which potentially cause more sewage discharge and/or water imports and thus increase low flows. Our detected percentage change in flow quantiles is consistent with unit changes reported by Anderson et al (2022), who found a 0.6-0.7 % increase in mean and high flows for 1 % increase in urban area using panel regression for 729 US catchments. Some studies have reported a higher value, for example, Blum et al (2020) found a 3.3 % increase in annual maximum flood for a 1 % increase in impervious basin cover using 280 US catchments; Yang et al (2021) found a 3.9 % increase in annual maximum discharge for a 1 % increase in urban area using 757 catchments in China.…”

“…Some studies have reported a higher value, for example, Blum et al (2020) found a 3.3 % increase in annual maximum flood for a 1 % increase in impervious basin cover using 280 US catchments; Yang et al (2021) found a 3.9 % increase in annual maximum discharge for a 1 % increase in urban area using 757 catchments in China. The variations in the magnitude may be due to the different statistical approaches (Anderson et al, 2022;Salavati et al, 2016), as well as the sampling sizes and methods (Blum et al 2020).…”

“…Quantifying the effect of urbanisation on river flow is not straightforward. Some authors have found that urbanisation increases flows, as impervious land cover leads to reduced infiltration, increased runoff volume, as well as shortened runoff response time (Blum et al, 2020;Cuo, 2016;De Niel and Willems, 2019;Prosdocimi et al, 2015;Teuling et al, 2019;Anderson et al, 2022). Various modelling approaches have been applied.…”

“…This could reflect differences in the methods applied (e.g. Anderson et al, 2022), or the difficulty in quantifying urbanisation (typically quantified as the catchment 'urban area fraction' or the 'mean areal imperviousness'). Such urbanisation metrics may not sufficiently explain the location, distribution, and character of impervious cover within a given catchment (Salavati et al, 2016).…”

Challenges in the attribution of river flood events

“…Observations show that deforestation in 56 developing countries increased flood occurrence during the last decades (Bradshaw et al, 2007). Similarly, Anderson et al (2022) show that urbanization and re-forestation have respectively increased and decreased extreme streamflow, in the context of 729 US catchments. Similar indications emerge from many modeling studies (e.g., Du et al, 2012).…”