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We employ the approach of Roderick and Farquhar (2011) to assess the sensitivity of runoff (R) given changes in precipitation (P), potential evapotranspiration (E p ), and other properties that change the partitioning of P (n) by estimating coefficients that predict the weight of each variable in the relative change of R. We use this framework using different data sources and products for P, actual evapotranspiration (E), and E p within the Amazon River basin to quantify the uncertainty of the hydrologic response at the subcatchment scale. We show that when estimating results from the different combinations of datasets for the entire river basin (at Óbidos), a 10% increase in P would increase R on average 16%, while a 10% increase in E p would decrease R about 6%. In addition, a 10% change in the parameter n would affect the hydrological response of the entire basin around 5%. However, results change from catchment to catchment and are dependent on the combination of datasets. Finally, results suggest that enhanced estimates of E and E p are needed to improve our understanding of the future scenarios of hydrological sensitivity with implications for the quantification of climate change impacts at the regional (subcatchment and subbasin) scale in Amazonia., (© 2020 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals LLC on behalf of New York Academy of Sciences.)

Quantifying the runoff uncertainty influenced by the climate drivers is essential for water resources management. Runoff in the Tarim River Basin (TRB) originated from the ice and snow meltwater is, thus, sensitive to climate change due to its inherent uncertainties. In this paper, a hybrid mathematical model for uncertainty estimation combing the water-heat coupling model, sensitivity coefficient, uncertainty measurement indices, and an improving standard uncertainty method was proposed to quantify the runoff uncertainty influenced by climate drivers in the TRB from 1965 to 2015. The results showed that the runoff uncertainty in the TRB varies at different time scales and bears strong uncertainty in summer and autumn. In addition, runoff is sensitive to climate change, and when the annual precipitation, annual average temperature, and annual potential evapotranspiration factors change by 1%, the annual runoff changes by 2.8857%, 1.6559%, and −1.8857%, respectively, while the contribution rate of temperature to runoff changes gradually increases. When the changes in precipitation and temperature have the fluctuation uncertainties of 5.76–6.96% and 22.26–66.72%, the runoff fluctuation uncertainties influenced by climate drivers in the Hotan River Basin, Yarkand River Basin, Aksu River Basin, and Kaikong River Basin are 14.31%, 15.03%, 18.23%, and 5.93%, respectively. The proposed hybrid mathematical model can provide a quantitative analysis reference for the runoff uncertainty under climate change in inland river basins in the arid region of Northwest China. [ABSTRACT FROM AUTHOR]