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 the knowledge and observations toward the effects are limited. 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 middle dams. We also test whether the effects increase or decrease with increasing initial values of human factors. This method is applied in 2739 hydrological stations in China. Results show that a 1 % increase of urban areas causes around a 2.9 % increase of Q. Cropland areas have no significant effect on Q. Reservoir index has a decreasing effect: a 1 unit increase of reservoir index causes a decrease in Q from 23.1 % to 5.4 % for catchments with initial reservoir indexes from 0 to 5. From 1992 to 2017, increasing urban areas cause more than 10 % increases in Q in 10.4 % of the 2739 catchments, most of which are located in the North Plain of China. From 1960 to 2017, increasing reservoir indexes cause a more than 10 % decrease of Q in 53.4 % of 777 catchments with at least one dam. Among 1074 catchments with limited impacts from urban areas and reservoir indexes, 210 (19.6 %) catchments have more than 10 % unexplained decreasing rates in Q per decade during 1960–2017, and 62.4 % of the 210 catchments are located in the middle and down streams of the Yellow River Basin and the upper streams of the Haihe 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

“…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.…”

Climate More Important for Chinese Flood Changes Than Reservoirs and Land Use

“…Similarly, other studies show reduction rates of floods of about 20%–80% in the middle Yellow River basin associated with a 28% increase in forest coverage during 1978–1997 (Bai et al., 2016; Gao et al., 2011). These findings have resulted in a perception that the construction of reservoirs and land use change have generally been dominant drivers of flood changes during the recent phase of rapid economic growth in China (see empirical analyses by W. Yang et al., 2020). However, these findings are based mostly on local studies and their regional scale relevance relative to that of climate change has so far not been elucidated.…”

The relative importance of antecedent soil moisture and precipitation in flood generation in the middle and lower Yangtze River basin

“…Information of 361 reservoirs in the middle and lower YZRB, including capacity and controlling area was downloaded and extracted from the Global Reservoir and Dam database (GRanD) (Lehner et al 2011). Previous study showed that this database provides reliable information of middle and large reservoirs in China (Yang et al 2021).…”