Unraveling the Role of Human Activities and Climate Variability in Water Level Changes in the Taihu Plain Using Artificial Neural Network

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)).

Seasonal Amplitude of Water Storage Variations of the Yangtze–Huai Plain Lake Group: Implicaion for Floodwater Storage Capacity

“…Poyang and Dongting Lakes are still naturally connected with the Yangtze River (Zhang et al, 2014;, with complex river-lake interaction and considerable seasonal amplitude in the water level cycle, which are 9.53 ± 2.02 m and 7.39 ± 1.29 m, respectively. On the other hand, Taihu Lake has the slightest seasonal dynamic change of water level (0.76 ± 0.42 m), mainly caused by artificial management such as the construction of embankments and dams (Wang J. et al, 2019;Wang et al, 2019c).…”

“…For instance, the inter-annual and seasonal fluctuation in the water area and water level of certain large lakes in the YHRB have been estimated using middle and high-resolution satellite image and altimetry satellite data. These lakes are mainly targeted in the top largest lakes, such as Poyang Lake (Song and Ke, 2014;Mei et al, 2015;Zeng et al, 2017;Wang et al, 2019b;Mu et al, 2020), Dongting Lake (Huang et al, 2011;Hu et al, 2015;Xing et al, 2018;Long et al, 2019;Wang et al, 2021a), Taihu Lake (Hu and Wang, 2009;Zhao et al, 2012;Wang et al, 2019c;Xu et al, 2020b), Hongze Lake (Yin et al, 2013;Cai et al, 2020;Mei et al, 2021) and Chaohu Lake (Chen et al, 2013;Lin et al, 2021), which have a significant impact on the surrounding ecological environment. At present, research related to water storage mostly exists in Poyang Lake (the largest freshwater lake in China) (Liu H. et al, 2020;Xu et al, 2020a;Song et al, 2021), while the estimate on seasonal water storage changes of other lakes in the YHRB still remains poorly quantified.…”

An Improved Genetic Algorithm Coupling a Back-Propagation Neural Network Model (IGA-BPNN) for Water-Level Predictions

“…The neural network, as a simulated biological neuron and highly parallel system [6,7], can solve the problem of model construction caused by the lack of characteristic hydrological parameters to a large extent, as well as simulating the temporal and spatial non-linear changes in hydrological systems [3]. Some studies have efficiently used a neural network for water-level forecasting [7][8][9][10][11][12][13]. Having considered data types used in the numerical model and other network models for waterlevel predictions, this research chose meteorological data, and water-level data, which mainly come from the China Meteorological Data Service Centre, maintained by the China Meteorological Administration (CMA) and Hubei Hydrographic Bureau (HHB), as experimental data.…”