scholarly journals Temporal Scaling of Water Level Fluctuations in Shallow Lakes and Its Impacts on the Lake Eco-Environments

2020 ◽  
Vol 12 (9) ◽  
pp. 3541
Author(s):  
Balati Maihemuti ◽  
Tayierjiang Aishan ◽  
Zibibula Simayi ◽  
Yilinuer Alifujiang ◽  
Shengtian Yang
Keyword(s):  
Water Balance ◽  
Water Level ◽  
Lake Water ◽  
Water Level Fluctuations ◽  
Balance Model ◽  
Lake Basin ◽  
Lake Ecosystems ◽  
Ebinur Lake ◽  
Ebinur Lake Basin ◽  
Lake Water Balance

Managing lake water levels from an ecological perspective has become an urgent issue in recent years in efforts to protect, conserve, and restore lake eco-environments. In this study, we considered the actual situation of Ebinur Lake basin to develop a lake water balance model using a System Dynamics (SD) method. The objective of this study is based on the lake water balance model to sufficiently understand the variation and relationship between the lake depth–area–volume. We combined field investigations and hydrological data analysis to expose the major factors affecting lake water level fluctuations (WLFs), as well as the impact of WLFs on lake eco-environments. All with the aim of providing a theoretical basis to manage Ebinur Lake ecosystems for conservation and restoration. The main findings of this study include: (I) The model’s calculation results agree with the observation value, as the monthly lake surface area was used to validate the model. (II) The factors influencing the dynamic changes in the water level of the lake are ranked in ascending order (from the lowest to the highest) as follows: Precipitation, groundwater recharge, evaporation, river inflow. (III) Fluctuations in water level play a significant role in lake shoreline displacement variation, and when the lake’s water level drops below 1 m, the surface area of the water body decreases to approximately 106 km2. (IV) The magnitude and frequency of WLFs drive major differences in the ecology of lake littoral zones, influencing not only the structure and functioning of benthic assemblages but also littoral habitat structure. These results established a quantitative linkage between hydrological variables and ecosystem health for the Ebinur Lake wetlands. These findings could be widely used in managing the Ebinur Lake basin as well as other similar water bodies, and could provide a useful tool for managing lake ecosystems for conservation and restoration.

IAEA Isotope-enabled coupled catchment–lake water balance model, IWBMIso: description and validation

2016 ◽  
Vol 52 (4-5) ◽  
pp. 427-442 ◽  
Author(s):  
Dagnachew Legesse Belachew ◽  
George Leavesley ◽  
Olaf David ◽  
Dave Patterson ◽  
Pradeep Aggarwal ◽  
...  
Keyword(s):  
Water Balance ◽  
Lake Water ◽  
Water Balance Model ◽  
Balance Model ◽  
Lake Water Balance

scholarly journals Examining the Driving Factors of the Direct Carbon Emissions of Households in the Ebinur Lake Basin Using the Extended STIRPAT Model

2021 ◽  
Vol 13 (3) ◽  
pp. 1339
Author(s):  
Ziyuan Chai ◽  
Zibibula Simayi ◽  
Zhihan Yang ◽  
Shengtian Yang
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Driving Factors ◽  
Series Data ◽  
Lake Basin ◽  
Stirpat Model ◽  
Consumption Structure ◽  
Ebinur Lake ◽  
Ebinur Lake Basin

In order to achieve the carbon emission reduction targets in Xinjiang, it has become a necessary condition to study the carbon emission of households in small and medium-sized cities in Xinjiang. This paper studies the direct carbon emissions of households (DCEH) in the Ebinur Lake Basin, and based on the extended STIRPAT model, using the 1987–2017 annual time series data of the Ebinur Lake Basin in Xinjiang to analyze the driving factors. The results indicate that DCEH in the Ebinur Lake Basin during the 31 years from 1987 to 2017 has generally increased and the energy structure of DCEH has undergone tremendous changes. The proportion of coal continues to decline, while the proportion of natural gas, gasoline and diesel is growing rapidly. The main positive driving factors affecting its carbon emissions are urbanization, vehicle ownership and GDP per capita, while the secondary driving factor is residents’ year-end savings. Population, carbon intensity and energy consumption structure have negative effects on carbon emissions, of which energy consumption structure is the main factor. In addition, there is an environmental Kuznets curve between DCEH and economic development, but it has not yet reached the inflection point.

scholarly journals Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang

2021 ◽  
Vol 13 (4) ◽  
pp. 769
Author(s):  
Xiaohang Li ◽  
Jianli Ding ◽  
Jie Liu ◽  
Xiangyu Ge ◽  
Junyong Zhang
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Prediction Accuracy ◽  
Lake Basin ◽  
Arid Areas ◽  
Annual Average ◽  
Machine Learning Methods ◽  
Ebinur Lake ◽  
Ebinur Lake Basin

As an important evaluation index of soil quality, soil organic carbon (SOC) plays an important role in soil health, ecological security, soil material cycle and global climate cycle. The use of multi-source remote sensing on soil organic carbon distribution has a certain auxiliary effect on the study of soil organic carbon storage and the regional ecological cycle. However, the study on SOC distribution in Ebinur Lake Basin in arid and semi-arid regions is limited to the mapping of measured data, and the soil mapping of SOC using remote sensing data needs to be studied. Whether different machine learning methods can improve prediction accuracy in mapping process is less studied in arid areas. Based on that, combined with the proposed problems, this study selected the typical area of the Ebinur Lake Basin in the arid region as the study area, took the sentinel data as the main data source, and used the Sentinel-1A (radar data), the Sentinel-2A and the Sentinel-3A (multispectral data), combined with 16 kinds of DEM derivatives and climate data (annual average temperature MAT, annual average precipitation MAP) as analysis. The five different types of data are reconstructed by spatial data and divided into four spatial resolutions (10, 100, 300, and 500 m). Seven models are constructed and predicted by machine learning methods RF and Cubist. The results show that the prediction accuracy of RF model is better than that of Cubist model, indicating that RF model is more suitable for small areas in arid areas. Among the three data sources, Sentinel-1A has the highest SOC prediction accuracy of 0.391 at 10 m resolution under the RF model. The results of the importance of environmental variables show that the importance of Flow Accumulation is higher in the RF model and the importance of SLOP in the DEM derivative is higher in the Cubist model. In the prediction results, SOC is mainly distributed in oasis and regions with more human activities, while SOC is less distributed in other regions. This study provides a certain reference value for the prediction of small-scale soil organic carbon spatial distribution by means of remote sensing and environmental factors.

Flow-Based Structural Modelling and Dynamic Simulation of Lake Water Levels

2011 ◽  
pp. 798-814
Author(s):  
Nashon Juma Adero ◽  
John Bosco Kyalo Kiema
Keyword(s):  
Water Balance ◽  
Water Level ◽  
Dynamic Simulation ◽  
Lake Water ◽  
Complex Dynamics ◽  
Water Levels ◽  
Major Question ◽  
Modern Computer ◽  
Hydrological System ◽  
Dynamic Simulation Model

The continuing decline in lake water levels is both a concern and daunting challenge to scientists and policymakers in this era, demanding a rethinking of technological and policy interventions in the context of broader political and socio-economic realities. It is self-evident that diverse factors interact in space and time in complex dynamics to cause these water-level changes. However, the major question demanding sound answers is how these factors interact and by what magnitude they affect lake water balance with time. This chapter uses Lake Victoria’s hydrological system to shed light on the extensive and flexible modelling and simulation capabilities availed by modern computer models to understand the bigger picture of water balance dynamics. The study used the 1950-2000 hydrological data and riparian population growth to develop a dynamic simulation model for the lake’s water level. The intuitive structure of the model provided clear insights into the combined influence of the main drivers of the lake’s water balance. The falling lake water levels appeared to be mainly due to dam outflows at the outlet and reduced rainfall over the lake. The ensuing conclusions stressed the need for checks against over-release of lake water for hydropower production and measures for sustainable land and water management in the entire basin.

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