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. 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 mediumdams. We also test whether the effects increase or decrease with increasinginitial values of human factors. This method is applied in 757 non-nestedcatchments in China. Results show that a 1 % point increase in urban areas causes around a 3.9 % increase in Q with a confidence intervalCI = [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.

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