Technical note: Long-term persistence loss of urban streams as a metric for catchment classification

Abstract

Abstract. Urbanisation has been associated with a reduction in the long-term correlation within a streamflow series, quantified by the Hurst exponent (H). This presents an opportunity to use the H exponent as an index for the classification of catchments on a scale from natural to urbanised conditions. However, before using the H exponent as a general index, the relationship between this exponent and level of urbanisation needs to be further examined and verified on catchments with different levels of imperviousness and from different climatic regions. In this study, the H exponent is estimated for 38 (deseasonalised) mean daily runoff time series, 22 from the USA and 16 from Australia, using the traditional rescaled-range statistic (R∕S) and the more advanced multifractal detrended fluctuation analysis (MF-DFA). Relationships between H and catchment imperviousness, catchment size, annual rainfall and specific mean discharge were investigated. No clear relationship with catchment area was found, and a weak negative relationship with annual rainfall and specific mean streamflow was found only when the R∕S method was used. Conversely, both methods showed decreasing values of H as catchment imperviousness increased. The H exponent decreased from around 1.0 for catchments in natural conditions to around 0.6 for highly urbanised catchments. Three significantly different ranges of H exponents were identified, allowing catchments to be parsed into groups with imperviousness lower than 5 % (natural), catchments with imperviousness between 5 and 15 % (peri-urban) and catchments with imperviousness larger than 15 % (urban). The H exponent thus represents a useful metric to quantitatively assess the impact of catchment imperviousness on streamflow regime.

A Global-Scale Investigation of Stochastic Similarities in Marginal Distribution and Dependence Structure of Key Hydrological-Cycle Processes

“…Therefore, based on the scientific boost, the climacogram (and not the other two metrics) was found to be adequate for the identification and model building of a stochastic process. Since then, interest in the scale domain and the climacogram estimator has increased, and the climacogram has been implemented in education material [49], and has been used to identify the LTP behaviour in various scientific studies, such as 2D precipitation fields [50], multidimensional spatiotemporal domain [51], paleoclimatic temperature [52] and precipitation [53,54], Bayesian statistical models of rainfall and temperature [55], higher-order moments of skewness and kurtosis vs. scale in grid turbulence [26], annual precipitation [56], water demand [57], daily river flows [58], precipitation and temperature for a bivariate drought analysis [59], wind and solar energy [60], water-energy nexus [61], solar radiation [62], wave height and period [63], daily streamflow [64], and monthly temperature and precipitation ( [65,66]), annual streamflow ( [30,66]), ecosystem variability [67], 2D rock formations [68], urban streamflows [69], global temperature and wind of resolution spanning 10 orders of magnitude from ms to several decades [70], disaggregation schemes from daily to hourly rainfall and runoff [71], hourly wind and daily precipitation [26], fine scale precipitation [3,22,[72][73][74][75][76][77][78], fine scale wind …”

Hydrological Spaces of Long‐Term Catchment Water Balance

“…Urbanization has been identified as a major driver of streamflow generation, with percentages of impervious areas as low as 20 % found to affect stream water quality and quantity (Jovanovic et al, ; Walsh et al, ). When isolating catchments with urban cover larger than 20% (121 catchments identified in Figure b), most of these follow the same patterns of the rest of the catchments, with the exception of five catchments estimated to have large ϕ (green and black dots in Figure b).…”

“…As shown in Figure 7a, when compared to Figure 5b, excluding catchments largely covered by ice and wetlands improves model results in terms of both scatter (R 2 increases from 0.82 to 0.89) and slope of the linear fitting between observed and modeled streamflow (the slope becomes closer to 1, increasing from 0.84 to 0.91, with a smaller intercept). Urbanization has been identified as a major driver of streamflow generation, with percentages of impervious areas as low as 20% found to affect stream water quality and quantity (Jovanovic et al, 2018;Walsh et al, 2005). When isolating catchments with urban cover larger than 20% (121 catchments identified in Figure 6b), most of these follow the same patterns of the rest of the catchments, with the exception of five catchments estimated to have large (green and black dots in Figure 6b).…”

Monitoring and assessment of endangered UNESCO World Heritage Sites using space technology: a case study of East Rennell, Solomon Islands

“…It is a mathematical tool to analyse the long-lasting nature and correlation of natural phenomena. It is widely used in hydrology, climate, and other fields [14][15][16]. Previous studies have shown that the Hurst exponent based on the rescaled range (R/S) is more stable than other methods [17].…”