Effects of Landscape Pattern Change on Water Yield and Nonpoint Source Pollution in the Hun-Taizi River Watershed, China

Zong, Min; Hu, Yuanman; Liu, Miao; Li, Chunlin; Wang, Cong; Ping, Xiaoying

doi:10.3390/ijerph17093060

Cited by 15 publications

(4 citation statements)

References 58 publications

(86 reference statements)

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“…With the increase of forest area, canopy interception of moisture and evapotranspiration has increased, resulting in decreased water yield. In this study, the landscape pattern index SHDI had weak explanatory power in affecting the water yield coefficient (the q value was less than 5%), which may be due to the large size of the calculated raster (Xu et al, 2019;Zong et al, 2020).…”

Section: Influence Of Land Use Factors On the Water Yield Coefficientmentioning

confidence: 62%

Evolution and attribution of the water yield coefficient in the Yiluo river basin

Hou

Yan

Qin

et al. 2022

Front. Environ. Sci.

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Our aim in this research was to detect historical and future water yield coefficient evolution and attribution. Based on the calibrated and validated water yield coefficient model in the Yiluo River Basin, the coefficient for the years 2000–2020 was simulated, along with the future projection for 2030–2050 under four Shared Socioeconomic Pathways (SSP126, SSP245, SSP370, and SSP585). The spatio-temporal evolution of historical and future water yield coefficients was then analyzed. Moreover, the geographical detector model was used to detect the impacts of climate, land use, and terrain factors on the water yield coefficient. The results showed that the water yield coefficient increased by 8.53% from 2000 to 2020, with the coefficient of farmland increasing by 10.47% and that of forestland decreasing by 8.93%. The coefficient was highest under the SSP370 scenario and the lowest under the SSP585 scenario in projections for 2030–2050. Compared to 2000–2020, the coefficients of the two scenarios increased by 12.2% and 2.0%, respectively. Consequently, under the SSP370 and SSP585 scenarios, the coefficient of farmland increased by 13.2% and 2.7%, and that of the forestland decreased by 0.9% and 14.6%, respectively. Driving factors detection indicated that land use types had the strongest explanatory power affecting the water yield coefficient; the explanatory value reached 26.5% in 2000–2020 and will exceed 29.5% in 2030–2050. In addition, the interaction between any two factors was stronger than a single factor. This research provides scientific support for the precise management of watershed and water-land resources.

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Section: Influence Of Land Use Factors On the Water Yield Coefficientmentioning

confidence: 62%

Evolution and attribution of the water yield coefficient in the Yiluo river basin

Hou

Yan

Qin

et al. 2022

Front. Environ. Sci.

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“…Prior to application, the SWAT model must be properly calibrated and validated to determine its reliability and accuracy. Methods and results for model calibration and validation are available in previous literature (Zong et al, 2020).…”

Section: Hydrological Processes -Swat Modelmentioning

confidence: 99%

Spatial planning and management to control multi-ecological environmental problems: A case study of Anshan city, Liaoning, China

Zhang

Liu

et al. 2023

Preprint

Self Cite

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Spatial pattern and process studies are of global interest and typically focus on one certain environmental or ecological process, and the methods employed mostly rely upon spatial process models. Meanwhile, spatial planning is an important administrative management in regional development, especially in China. However, traditional urban planners focused more on social economy and less on eco-environment. How to conduct scientific planning is the key to solving and preventing eco-environmental problems. In this study, we proposed a synthetic spatial analysis and planning method that involves atmospheric, edaphic, hydrographic and ecological processes to zoning management and control strategies for regional sustainable goals. The method can identify key eco-environmental processes and regions effectively, divide scientific spatial control unit, and set precise management polices to prevent environmental pollution and ecological damage events. The synthetic method was conducted using advanced multi-process models and spatial statistical methods, including CMAQ and SWAT models. The case study in Anshan city indicated that the models we proposed can effectively reflect the link between pollutants and land use pattern. According to the process simulation results of pollutant distribution, we combined the administrative districts and land use, divided 67 types of eco-environmental management units based on atmospheric pollution discharge, hydrographic pollution discharge, edaphic heavy metal pollution, and ecological space. For each unit, we proposed feasible management policies and control measures. This synthetic method can provide scientific analysis and guidance support spatial planning and ecological construction for multi-purpose ecological environmental protection.

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“…The Haihe river has the comprehensive functions of drainage, storage, water supply, shipping, tourism, and environmental protection and is one of the significant water resources in Tianjin. Under the background of rapid urbanization and economic development, water shortages and water degradation have become increasingly severe in many watersheds in China [36]. In all seven river basins, the Haihe basin is suffering the most water shortages and water pollution [37].…”

Section: Study Areamentioning

confidence: 99%

Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing

Cao

Sun

et al. 2021

Water

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The Haihe River is a typical sluice-controlled river in the north of China. The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water environment control in northern rivers. In recent years, remote sensing has been widely used in water quality monitoring. However, due to the low signal-to-noise ratio (SNR) and the limitation of instrument resolution, satellite remote sensing is still a challenge to inland water quality monitoring. Ground-based hyperspectral remote sensing has a high temporal-spatial resolution and can be simply fixed in the water edge to achieve real-time continuous detection. A combination of hyperspectral remote sensing devices and BP neural networks is used in the current research to invert water quality parameters. The measured values and remote sensing reflectance of eight water quality parameters (chlorophyll-a (Chl-a), phycocyanin (PC), total suspended sediments (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH4-N), nitrate-nitrogen (NO3-N), and pH) were modeled and verified. The results show that the performance R2 of the training model is above 80%, and the performance R2 of the verification model is above 70%. In the training model, the highest fitting degree is TN (R2 = 1, RMSE = 0.0012 mg/L), and the lowest fitting degree is PC (R2 = 0.87, RMSE = 0.0011 mg/L). Therefore, the application of hyperspectral remote sensing technology to water quality detection in the Haihe River is a feasible method. The model built in the hyperspectral remote sensing equipment can help decision-makers to easily understand the real-time changes of water quality parameters.

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Effects of Landscape Pattern Change on Water Yield and Nonpoint Source Pollution in the Hun-Taizi River Watershed, China

Cited by 15 publications

References 58 publications

Evolution and attribution of the water yield coefficient in the Yiluo river basin

Evolution and attribution of the water yield coefficient in the Yiluo river basin

Spatial planning and management to control multi-ecological environmental problems: A case study of Anshan city, Liaoning, China

Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing

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