Attribution of climate change and human activities to streamflow variations with a posterior distribution of hydrological simulations
Authors: Tang Xiongpeng , Fu Guobin , Zhang Silong , Gao Chao , Wang Guoqing , Bao Zhenxin , Liu Yanli , Liu Cuishan , Jin Junliang 
Published: October 2022

Abstract. Hydrological simulations are a main method of quantifying
the contribution rate (CR) of climate change (CC) and human activities (HAs)
to watershed streamflow changes. However, the uncertainty of hydrological
simulations is rarely considered in current research. To fill this research
gap, based on the Soil and Water Assessment Tool (SWAT) model, in this
study, we propose a new framework to quantify the CR of CC and HAs based on
the posterior histogram distribution of hydrological simulations. In our new
quantitative framework, the uncertainty of hydrological simulations is first
considered to quantify the impact of “equifinality for different
parameters”, which is common in hydrological simulations. The Lancang River
(LR) basin in China, which has been greatly affected by HAs in the past 2 decades, is then selected as the study area. The global gridded monthly
sectoral water use data set (GMSWU), coupled with the dead capacity data of
the large reservoirs within the LR basin and the Budyko hypothesis
framework, is used to compare the calculation result of the novel framework. The results show that (1) the annual streamflow at Yunjinghong
station in the Lancang River basin changed abruptly in 2005, which was mainly due to the construction of the Xiaowan hydropower station that
started in October 2004. The annual streamflow and annual mean temperature
time series from 1961 to 2015 in the LR basin showed significant decreasing and increasing trends at the α= 0.01 significance level, respectively. The annual precipitation showed an insignificant
decreasing trend. (2) The results of quantitative analysis using the new
framework showed that the reason for the decrease in the streamflow at
Yunjinghong station was 42.6 % due to CC, and the remaining 57.4 % was
due to HAs, such as the construction of hydropower stations within the study
area. (3) The comparison with the other two methods showed that the CR of CC
calculated by the Budyko framework and the GMSWU data was 37.2 % and 42.5 %, respectively, and the errors of the calculations of the new
framework proposed in this study were within 5 %. Therefore, the newly
proposed framework, which considers the uncertainty of hydrological
simulations, can accurately quantify the CR of CC and HAs to streamflow
changes. (4) The quantitative results calculated by using the simulation
results with the largest Nash–Sutcliffe efficiency coefficient (NSE) indicated that CC was the dominant factor in streamflow reduction, which was in opposition to the calculation results of our new framework. In other
words, our novel framework could effectively solve the calculation errors
caused by the “equifinality for different parameters” of hydrological
simulations. (5) The results of this case study also showed that the
reduction in the streamflow in June and November was mainly caused by
decreased precipitation and increased evapotranspiration, while the changes
in the streamflow in other months were mainly due to HAs such as the
regulation of the constructed reservoirs. In general, the novel quantitative
framework that considers the uncertainty of hydrological simulations
presented in this study has validated an efficient alternative for
quantifying the CR of CC and HAs to streamflow changes.