Abstract
Water level, as a key indicator for the floodplain area, has been largely affected by the interplay of climate variability and human activities during the past few decades. Due to a nonlinear dependence of water level changes on these factors, a nonlinear model is needed to more realistically estimate their relative contribution. In this study, the attribution analysis of long-term water level changes was performed by incorporating multilayer perceptron (MLP) artificial neural network. We took the Taihu Plain in China as a case study where water level series (1954–2014) were divided into baseline (1954–1987) and evaluation (1988–2014) periods based on abrupt change detection. The results indicate that climate variables are the dominant driver for annual and seasonal water level changes during the evaluation period, with the best performance of the MLP model having precipitation, evaporation, and tide level as inputs. In the evaluation period, the contribution of human activities to water level changes in the 2000s is higher than that in the 1990s, which indicates that human activities, including the rapid urbanization, are playing an important role in recent years. The influence of human activities, especially engineering operations, on water level changes in the 2000s is more evident during the dry season (March-April-May (MAM) and December-January-February (DJF)).
Highlights
It is widely recognized that climate variability and human activities have exerted considerable impacts on hydrological processes globally during the past decades [1,2,3,4]
The results show that the null hypothesis H0 of the Mann-Whitney test is rejected at the 95% confidence level, implying significant changes in the annual and seasonal water level series between the two sub-periods
It is clear that abrupt changes of water level in seasonal water level across the
Summary
It is widely recognized that climate variability and human activities have exerted considerable impacts on hydrological processes globally during the past decades [1,2,3,4]. The influence of human activities, such as landscape change, hydraulic construction, and river channelization, make hydrological processes more complex [8,9,10]. In order to effectively manage regional water resources, it is important to investigate the influence of both climate variability and human activities on hydrological processes. The water level is an important indicator of surface water studies and flood management across the floodplain areas [11,12]. Some researchers have investigated the influence of either climate variability or anthropogenic activities on water level variations [15,16]. Both climatic and anthropogenic factors should be considered for the
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