scholarly journals A Settlement Landscape Reconstruction Approach Using GIS Analysis with Integrated Terrain Data of Land and Water: A Case Study of the Panlongcheng Site in the Shang Dynasty (Wuhan, China)

2021 ◽  
Vol 13 (24) ◽  
pp. 5087
Author(s):  
Jianfeng Liu ◽  
Qiushi Zou ◽  
Qingwu Hu ◽  
Changping Zhang
Keyword(s):  
Water Level ◽  
Human Activities ◽  
Spatial Information ◽  
Yangtze River Basin ◽  
Water Area ◽  
Shang Dynasty ◽  
Landscape Reconstruction ◽  
Water Level Changes ◽  
History Of ◽  
Spatial Information Technology

The landscape of ancient sites has changed greatly with the passage of time. Among all of the factors, human activities and the change in natural environment are the main factors leading to the change in site landscape. The Panlongcheng site, which is located in Hubei Province, China, has a history of 3500 years with the most abundant relics in the Yangtze River Basin during the Shang Dynasty. As a near-water site, the landscape of the Panlongcheng site is greatly affected by water level changes and water conservancy activities. In this paper, by using spatial information technology, the data obtained from land and underwater archaeological exploration were integrated to restore landscapes of Panlongcheng sites in different periods. After removing modern artificial features and topsoil, the landscapes of the sites before the Shang Dynasty, in the Shang Dynasty and modern time were reconstructed. Combining historical records of water level changes, the landscape and water–land distribution of the Panlongcheng site were compared. The analysis results reflect the interaction between water level changes and human activities in this region for thousands of years, and support the archaeological findings in the near-water area of the Panlongcheng site, which provides a new idea for the landscape reconstruction and analysis of near-water sites.

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

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 720 ◽  
Author(s):  
Yuefeng Wang ◽  
Hossein Tabari ◽  
Youpeng Xu ◽  
Yu Xu ◽  
Qiang Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Climate Variability ◽  
Water Level ◽  
Human Activities ◽  
Nonlinear Dependence ◽  
Rapid Urbanization ◽  
Evaluation Period ◽  
Water Level Changes ◽  
Artificial Neural

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)).

scholarly journals Estimating Real-Time Water Area of Dongting Lake Using Water Level Information

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1240
Author(s):  
Yuannan Long ◽  
Rong Tang ◽  
Changshan Wu ◽  
Changbo Jiang ◽  
Shixiong Hu
Keyword(s):  
Linear Regression ◽  
Water Level ◽  
Real Time ◽  
Yangtze River Basin ◽  
Water Area ◽  
Dongting Lake ◽  
Water Levels ◽  
Lake Area ◽  
Level Information ◽  
Maximum Water

Dongting Lake, the second largest freshwater lake in China, is an important water source for the Yangtze River Basin. The water area of Dongting Lake fluctuates significantly daily, which may cause flooding and other relevant disasters. Although remote sensing techniques may provide lake area estimates with reasonable accuracy, they are not available in real-time and may be susceptible to weather conditions. To address this issue, this paper attempted to examine the relationship between lake area and the water levels at the hydrological stations. Multi-temporal water area data were derived through analyzing Moderate Resolution Imaging Spectroradiometer (MODIS) imagery using the Automatic Water Extraction Index (AWEI). Then we analyzed the inter- and intra-annual variations in the water area of the Dongting Lake. Corresponding water level information at hydrological stations of the Dongting Lake were obtained. Simple linear regression (SLR) models and stepwise multiple linear regression (SMLR) models were constructed using water levels and water level differences from the upstream and downstream hydrological stations. We used the data from 2004 to 2012 and 2012, respectively, to build the model, and applied the data from 2013 to 2015 to evaluate the models. Results suggest that the maximum water area of the Dongting Lake during 2000–2015 has a clear decreasing trend. The variations in the water area were characterized by hydrological seasons, with the annual minimum and maximum water areas occurring in January and September, respectively. The water level at the Chengjingji station, and water level differences between upstream stations and the Chengjingji station, play a major role in estimating the water area. Further, results also show that the SMLR established in 2012 performs the best in estimating water area of the Dongting Lake, especially with high water levels.

scholarly journals Assessing the Influence of Compounding Factors to the Water Level Variation of Erhai Lake

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 29
Author(s):  
Huaxin Wu ◽  
Shengrui Wang ◽  
Tao Wu ◽  
Bo Yao ◽  
Zhaokui Ni
Keyword(s):  
Climate Change ◽  
Water Level ◽  
Water Resource ◽  
Lake Water ◽  
Human Activities ◽  
Resource Planning ◽  
Test Method ◽  
Erhai Lake ◽  
Water Level Changes ◽  
The Impact

Climate change and human activities cause lake water level (WL) fluctuations to exceed natural thresholds, with implications for the available water resources. Studies that explore WL change trends and the main driving forces that affect water level changes are essential for future lake water resource planning. This study uses the Mann–Kendall trend test method to explore the WL fluctuations trend and WL mutation in Erhai Lake (EL) during 1990–2019 and explore the main driving factors affecting water level changes, such as characteristic WL adjustments. We also use the principal component analysis to quantify the contribution of compound influencing factors to the water level change in different periods. The results showed that the WL rose at a rate of 47 mm/a during 1990–2019 but was influenced by the characteristic WL adjustment of EL in 2004 and the WL mutation in 2005. In 1990–2004, the WL showed a downtrend caused by the increase in water resource development and utilization intensity, and in 2005–2019, the WL showed an uptrend caused by the combined decrease in evaporation, outflow, and the increase in water supply for water conservancy projects. Additionally, the largest contributions of outflow to WL change were 19.34% and 21.61% in 1990–2019 and 1990–2004, respectively, while the largest contribution of cultivated area to WL change was 20.48% in 2005–2019, and it is worth noting that the largest contribution of climate change to WL change was 40.35% in 2013–2019. In the future, under the increase in outflow and evaporation and the interception of inflow, the WL will decline (Hurst exponent = 0.048). Therefore, planning for the protection and management of lakes should consider the impact of human activities, while also paying attention to the influence of climate change.

Maps showing water-level changes in six areas in Utah

1962 ◽  
Author(s):  
G.W. Sandberg ◽  
R.G. Butler ◽  
Joseph Spencer Gates
Keyword(s):  
Water Level ◽  
Water Level Changes
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