Causal effects of dams and land cover changes on flood changes in mainland China

Abstract

Abstract. Quantifying the effects of human activities on floods is challenging because of limited knowledge and observations. Many previous methods fail to isolate different effects and reduce the uncertainty caused by small samples. We use panel regressions to derive the sensitivity of annual maximum discharges (Q) to the changing values of three human factors: urban areas, cropland areas, and reservoir indexes for large and medium dams. We also test whether the effects increase or decrease with increasing initial values of human factors. This method is applied in 757 non-nested catchments in China. Results show that a 1 % point increase in urban areas causes around a 3.9 % increase in Q with a confidence interval CI = [1.9 %, 5.7 %]. Cropland areas have no significant effect on Q. Reservoir index has a decreasing effect: a 1 unit increase in reservoir index causes a decrease in Q from 21.4 % (with CI = [11.4 %, 29.9 %]) to 6.2 % (with CI = [3.2 %, 9.1 %]) for catchments with initial reservoir indexes from 0 to 3. Among 61 catchments with significant increases in observed Q in 1992–2017, increasing urban areas cause more than 10 % increases in Q in only five (8.2 % of 61) catchments. Among 234 catchments with at least one dam and significant decreases in observed Q in 1960–2017, increasing reservoir indexes cause more than 10 % decreases in Q in 138 (59.8 % of 234) catchments. Among 1249 catchments with limited impacts from urban areas and reservoir indexes, 403 (32.3 %) catchments have significant decreases in Q during 1960–2017, and 46.7 % of the 403 catchments are located in the middle and downstream of the Yellow River Basin and the upper streams of the Hai He River Basin. This study extends the panel regression method in hydrology and sheds light on the attribution of flood changes on a national scale.

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

“…It is possible that the difference in our results when compared to Blum et al (2020) relate to the urbanization data, which was created from a different origin. However, Yang et al (2021), also use the ESA CCI land cover data for a similar application in China, and estimate an approximately 3.9% effect of a 1%-point increase in urban area on annual floods in a sample of 757 catchments. It is more likely then, that the difference in results relates to the segment of the streamflow hydrograph which is being examined.…”

“…Panel regression models have recently been applied in hydrological research (Blum et al, 2020;Davenport et al, 2020;De Niel & Willems, 2019;Ferreira & Ghimire, 2012;Levy et al, 2018;Müller & Levy, 2019;Yang et al, 2021). The approach allows consideration of the data across both time and space; here we quantify the average effects of individual drivers (changes in tree cover and urban area) across all sites while controlling for the influence of a wide range of confounding variables (Figure 2).…”

“…First, a single-catchment approach involves fitting distinct regression models to often "lumped" time-series data for individual catchments, then assessing the fit of these models and signs of their coefficients (e.g., Neri et al, 2019;Prosdocimi et al, 2015;Slater, Anderson, et al, 2021;Villarini et al, 2009). Alternatively, a combined multi-catchment approach involves fitting panel regression models to estimate average causal effects across many sites (recent examples in hydrology include: Bassiouni et al, 2016;Blum et al, 2020;Brady et al, 2019;De Niel & Willems, 2019;Lombard & Holtschlag, 2018;Steinschneider et al, 2013;Yang et al, 2021). The deceptively simple nature of regression approaches means that they have been widely applied, however, while both single-catchment and multi-catchment approaches have their unique benefits, they are best suited to slightly different questions.…”

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

“…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. 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).…”

Toward a Catchment‐Scale Assessment of Flood Peak Attenuation by Multiple Reservoirs

“…Gao et al, 2019;Y. Gao et al, 2009;Graf, 1999Graf, , 2006Jiang et al, 2021;Mei et al, 2017;Singer, 2007;Wang et al, 2017;Xiong et al, 2019;Yang et al, 2021;Zhang et al, 2014).…”