Next Article in Journal
Behavior of TiO2 and CeO2 Nanoparticles and Polystyrene Nanoplastics in Bottled Mineral, Drinking and Lake Geneva Waters. Impact of Water Hardness and Natural Organic Matter on Nanoparticle Surface Properties and Aggregation
Previous Article in Journal
Spectrophotometric Detection of Glyphosate in Water by Complex Formation between Bis 5-Phenyldipyrrinate of Nickel (II) and Glyphosate
Previous Article in Special Issue
Spatial Downscaling of Tropical Rainfall Measuring Mission (TRMM) Annual and Monthly Precipitation Data over the Middle and Lower Reaches of the Yangtze River Basin, China
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Water 2019, 11(4), 720; https://doi.org/10.3390/w11040720

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

1
School of Geography and Tourism, Chongqing Normal University, Chongqing 401331, China
2
School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China
3
Hydraulics Section, Department of Civil Engineering, KU Leuven, 3000 Leuven, Belgium
*
Author to whom correspondence should be addressed.
Received: 22 February 2019 / Revised: 22 March 2019 / Accepted: 2 April 2019 / Published: 6 April 2019
(This article belongs to the Special Issue Hydrological Impacts of Climate Change and Land Use/Land Cover Change)
  |  
PDF [2234 KB, uploaded 21 April 2019]
  |  

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)). View Full-Text
Keywords: water level variation; contribution analysis; multilayer perceptron network; human activities; climate variability water level variation; contribution analysis; multilayer perceptron network; human activities; climate variability
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Wang, Y.; Tabari, H.; Xu, Y.; Xu, Y.; Wang, Q. Unraveling the Role of Human Activities and Climate Variability in Water Level Changes in the Taihu Plain Using Artificial Neural Network. Water 2019, 11, 720.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top