Landscape- and climate change-induced hydrological alterations in the typically urbanized Beiyun River basin, Beijing, China

Zhang, Yueqiu; Liu, Shiliang; Hou, Xiyong; Cheng, Fangyan; Shen, Zhenyao

doi:10.1007/s00477-018-1628-8

Cited by 12 publications

(4 citation statements)

References 53 publications

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“…The China Soil Map in the HWSD (Harmonized World Soil Database) was re‐projected and extracted to build the soil database in SWAT. The resolution and scale of the soil data are 1 km and 1:1,000,000, respectively (Zhang et al, 2019a, 2019b, 2019c). The SWAT soil database does not match the HWSD, thus the soil properties were calculated and integrated into SWAT to establish a new soil database (Singh et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, some artificial measures (e.g., terraces and check‐dams) influence the runoff process by altering the slope, slope length, and other factors of the original terrain (Tian et al, 2015; Xu et al, 2018). Measures adopted by the GLCP also change the land use/cover and topography of the watershed (Zhang, Liu, et al, 2019), and the impact of these changes on the extreme water runoff needs to be evaluated urgently.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the impact of the Gully Land Consolidation Project on runoff under extreme rainfall

et al. 2022

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Extreme rainfall is an important driver of soil erosion and land damage. The Gully Land Consolidation program (GLCP) was first launched in 2011 as a major land reclamation practice to increase farmland in the Loess Plateau of China. Studying the impact of artificial projects on hydrology can help humans to respond to the various water issues, but the assessment of the effects of the GLCP on extreme rainfallinduced water runoff at watershed scale is currently lacking. Our study used the soil and water assessment tool (SWAT) to evaluate the influence of the GLCP at different locations and areas on water runoff under extreme rainfall events in the Yanhe watershed. Results showed that: (1) the GLCP can improve the interception of surface runoff, with interception efficiency in downstream of the watershed approximately twice that at midstream and entire watershed as well as seven-times that at the upstream; (2) when GLCP measures are evenly distributed in a watershed, as the area of GLCP increases from 76.40 km 2 (1% of watershed area) to 382.01 km 2 (5%), the interception of surface runoff increases by 0.77 mm; (3) and the GLCP can increase soil infiltration and groundwater recharge. This research is expected to provide insights into the optimized layout of the GLCP at watershed scale. Correspondingly, policymakers can refer to this information in developing policies on the sustainable use of land.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of the impact of the Gully Land Consolidation Project on runoff under extreme rainfall

et al. 2022

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“…(2) The Sankey diagram was made by origin software to reveal the conversion characteristics of the various land use types in different years (Zhang et al, 2019). The map depicts a network between start and end states, while the different branching lines inside the network represent the conversion of land use types.…”

Section: Ca-markov Modelmentioning

confidence: 99%

Spatial response of urban land use change and ecosystem service value in the lower reaches of the Yangtze River: A case study of Tongling, China

Liu

Qin

Liu

et al. 2023

Front. Environ. Sci.

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Exploring the impact of urban land use change (LUC) on ecosystem services is significant for ensuring urban ecological security and realizing regional economic development. This study used land use data from 1985, 2000, 2014, and 2020 of Tongling city and the CA–Markov model to predict the LUC in 2025 and 2030. The spatio-temporal distribution characteristics of LUC during 1985–2030 were analyzed using the land use dynamics model and Sankey diagram. The vegetation-corrected ecological services value (ESV) equivalent was used to quantify the impact of LUC on ESV. Estimating the profit/loss value and spatial autocorrelation revealed ESV’s spatial and temporal dynamics in Tongling city. The results show that: 1) The most obvious LUC in the study area during 1985–2030 is the conversion of cultivated land to built-up land, in which the proportion of cultivated land decreased by 9.6%, and built-up land increased by 12.2%. 2) During 1985–2030, the ESV in Tongling showed an increasing trend at the beginning and then decreased, reaching a maximum value of 274.74 billion yuan in 2000. Regarding individual ecosystem service functions, the hydrological regulation function significantly contributed to ESV changes. The area change in paddy fields and built-up land had the most significant impact on ESV. 3) The total addition of ESV in Tongling during 1985–2030 was 5.98 billion yuan; the total loss was 18.59 billion yuan, and the net loss was 12.6 billion yuan. The proportion of area with ESV gains relative to the whole city is getting smaller, and the proportion of area with ESV losses is growing. The spatial autocorrelation shows the presence of high aggregation in ESV profit and loss areas. Simulating future LUC in Tongling and exploring ESV’s response is beneficial to developing new landscape patterns and ecological protection. It also provides a scientific basis for the extensive promotion of sustainable urban development in the future.

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“…The BFI can re ect the base ow and stream ow status of the catchment, which is closely related to the catchment characteristics, such as the soil characteristics, the geological conditions, and the topographical factors (Henderson and Wooding 1964;Zhang et al 2019b). According to previous studies, the BFI is often used as a hydrological factor to re ect the low ow conditions of catchments (Loveridge and Rahman 2014;Seo et al 2018;Yang et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Catchment natural driving factors and prediction of baseflow index for Continental United States based on Random Forest technique

Huang

Dong

Zhang

et al. 2021

Stoch Environ Res Risk Assess

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Base ow plays a critical role in maintaining the aquatic environmental health. However, the driving factors and predictions of base ow have not been rigorously investigated on a large scale, partly preventing hydrologist from deeply understanding runoff generation. To this end, the Lyne-Hollick (LH) digital lter method and the automatic base ow identi cation technique (ABIT) were used to estimate the long-term and seasonal base ow index (BFI) of 619 catchments across Continental United States (CONUS) from 1981 to 2014. Six natural driving factors are selected from the 31 catchment attributes about topography and location, soil, geology, land cover, and climate characteristics. The Random Forest (RF) technique was used to predict the BFI with the selected six driving factors as predictors. Results show that the long-term average BFI was 0.49, and the BFI value was different in four seasons, with the highest value of 0.55 in winter and the lowest value of 0.46 in autumn. The forest fraction, clay proportion and snow fraction were the most powerful factors affecting the long-term average BFI. The RF technique predicts the BFI across the 619 sites in CONUS with a R 2 of 0.59 after Leave-One-Location cross-validation, which was more satisfactory than the multiple linear regression method. This study can provide a deep insight into the generation and variation of base ow and guide the annual base ow prediction for water resources management.

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Landscape- and climate change-induced hydrological alterations in the typically urbanized Beiyun River basin, Beijing, China

Cited by 12 publications

References 53 publications

Evaluation of the impact of the Gully Land Consolidation Project on runoff under extreme rainfall

Evaluation of the impact of the Gully Land Consolidation Project on runoff under extreme rainfall

Spatial response of urban land use change and ecosystem service value in the lower reaches of the Yangtze River: A case study of Tongling, China

Catchment natural driving factors and prediction of baseflow index for Continental United States based on Random Forest technique

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