Socio-ecological determinants on spatio-temporal changes of groundwater in the Yellow River Basin, China

Lin, Mi; Biswas, Asim; Bennett, Elena M.

doi:10.1016/j.scitotenv.2020.138725

Cited by 28 publications

(8 citation statements)

References 46 publications

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“…In addition, two NDVI data products were preprocessed using the MODIS Reprojection Tool (MRT) (H. Y. Zhang et al, 2018). To reduce the differences caused by different sensors, we adopted the spatiotemporal consistency and statistical downscaling (N. Li, Zhang, et al, 2022; Lin et al, 2020) method to correct NDVI data, and then used the maximum value synthesis method (MVC) and Savitzky–Golay filter to obtain monthly NDVI data from 1985 to 2021 to calculate vegetation coverage (FVC). We used an image dichotomous model (Mu et al, 2017) to calculate FVC from NDVI, and the pure image pixel values of vegetation and bare soil depended on the 95% and 5% confidence levels of NDVI pixel statistics, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…Zhang et al, 2018). To reduce the differences caused by different sensors, we adopted the spatiotemporal consistency and statistical downscaling (N. Li, Zhang, et al, 2022;Lin et al, 2020) method to correct NDVI data, and then used the maximum value synthesis method (MVC) and Savitzky-Golay filter to obtain monthly NDVI data from 1985 to 2021 to calculate vegetation coverage (FVC). We used an image dichotomous model (Mu et al, 2017)…”

Section: Data Preparation and Preprocessingmentioning

confidence: 99%

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Human activities are the key driver of water erosion changes in northeastern China

Tang,

Hua,

Cao

et al. 2023

Land Degrad Dev

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Northeast China is experiencing severe soil erosion, resulting in land degradation and nutrient loss. Among them, water erosion poses the greatest threat to food security. Combining various multi‐source data, we applied the modified soil erosion equation (RUSLE) to evaluate the water erosion modulus in Northeast China (1985–2021). We used the threshold segmentation and residual analysis method to quantify the relative roles of climate change and human activities on water erosion. The outcomes were as follows: (1) In recent decades, the average annual water erosion modulus in northeastern China was 804.78 t·km−2·a−1, decreasing with a slope of −11.114 t·km−2·a−1. In 24% of the study area, the erosion modulus increased significantly, mainly in grassland and farmland, due to grassland degradation and expansion of sloping cropland. In 20% of the study area, erosion modulus decreased significantly, mainly spread in forests, which was attributed to the implementation of important ecological restoration projects by the Chinese government, such as afforestation programs and returning farmland to forest. (2) During the research process, there were spatial variations in water erosion drivers in Northeast China. For areas with significant increases in erosion, 20% of the total study area were caused by anthropogenic factors, and 3% were attributed to natural factors. For areas where erosion had significantly improved, 18% of the total study area was attributed to ecological restoration, and 2% to natural factors. (3) Based on the results of the study, ecological restoration of forests and grasses, management of sloping cropland, and conservation tillage were recommended to reduce soil erosion in Northeast China.

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Section: Methodsmentioning

confidence: 99%

Section: Data Preparation and Preprocessingmentioning

confidence: 99%

Human activities are the key driver of water erosion changes in northeastern China

Tang,

Hua,

Cao

et al. 2023

Land Degrad Dev

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“…Human factors include population density, changes in land use, water consumption for irrigation and domestic purposes etc. (Ojeda Olivares et al, 2019;Xia et al, 2019;Lin et al, 2020). Natural factors can be classified into terrestrial and hydrological factors.…”

Section: Variable Selectionmentioning

confidence: 99%

“…Natural factors include terrestrial, hydrology, and climate change, while human factors include land-use changes, river regulation, afforestation and deforestation, and groundwater extraction (Fu et al, 2019;Ojeda Olivares et al, 2019;Parizi et al, 2020;Van Huijgevoort et al, 2020;Ebrahimi et al, 2021;Maihemuti et al, 2021;Wu et al, 2021). Researchers have used various statistical regression models for understanding the drivers of changes in GWL analysis (Ainiwaer et al, 2019;Fu et al, 2019;Lin et al, 2020;Li et al, 2020;Mulyadi et al, 2020;Wu et al, 2021). Statistical regression analysis methods include the multiple linear regression model and geographically weighted regression (GWR) model (Brunsdon et al, 1998).…”

Section: Introductionmentioning

confidence: 99%

Spatial correlation of groundwater level with natural factors using geographically weighted regression model in the Choushui River Alluvial Fan, Taiwan

et al. 2022

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The groundwater of the Choushui River alluvial fan in Central Taiwan has been overexploited for a long time. It is essential to understand the factors governing changes in groundwater level (GWL) for the use of water resources. In this study, we first conducted a Mann–Kendall test to identify significant trends in the regional GWL and obtained its spatial characteristics using the Moran’s I index in the Choushui River alluvial fan. Furthermore, we established a geographically weighted regression (GWR) model to explore the spatial correlation between natural factors and GWL in dry and wet seasons from 1999 to 2019. The long-term trend analysis shows that the GWL of the Choushui River alluvial fan decline significantly. The Moran’s I index shows that the spatial distribution of GWL had a positive correlation in both dry and wet seasons. GWR model indicate that the GWL are affected by drainage density (Dd), slope (S), normalized difference vegetation index (NDVI), and precipitation (P) during the dry season, while Dd, S, NDVI, and wetness index (WI) have an effect on the GWL during the wet season. These results can not only describe the model applicability for exploring the relationship between natural factors and GWL but also be used as references for future regional water resource utilization and management.

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“…For example, climatic factors (such as precipitation) can directly affect TWS by decreasing or increasing the input of the water cycle, and at the same time, indirectly affect the TWS by affecting human water use and water uptake by vegetation [38][39][40]. Recently, Lin et al [41] discussed the influence of interaction between variables on the change in groundwater storage at temporal and spatial scales based on principal component analysis. However, the study did not explicitly provide interactive variables and their relative importance, and the agreement between the measured value and the simulated value was low at the spatial scale.…”

Section: Introductionmentioning

confidence: 99%

Spatial Variations in Terrestrial Water Storage with Variable Forces across the Yellow River Basin

et al. 2021

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Terrestrial water storage (TWS) variations are a result of the interconnected impact of various variables including climate, hydrology, ecology, and anthropogenic activities. Previous studies have indicated that climate factors (e.g., precipitation and potential evapotranspiration), vegetation restoration, and water withdrawals (irrigational and industrial water use) are the major determinants of TWS depletion across the Yellow River Basin (YRB). However, few studies have provided explicit information about the main forcing variables that determine spatiotemporal variations in TWS and the synergies among these factors. This study explored the explicit understanding of hydro-climatic and socio-ecological determinants and the key interacting processes that affected the TWS variations across the Yellow River Basin in northern China. The multivariate adaptive regression splines model was employed to establish the relationship function of the long-term trends for the dependent (TWS) and independent (explanatory) variables consisting of normalized difference vegetation index (NDVI), hydro-climate, and human water withdrawal. The long-term trends estimated from the MARS model reproduced the ones calculated by Gravity Recovery and Climate Experiment gravity satellites, with a determination coefficient (R2) of 0.83 and a mean absolute error (MAE) of 1.2 mm. The results showed that precipitation, minimum temperature, runoff, base flow, water withdrawal for electricity, and NDVI were the main drivers of the spatiotemporal variations in the TWS, of which minimum temperature and runoff played a considerable role in TWS variations through the interplay with other variables. The critical values of the trend for interactive variables, which could alter the acting direction of the synergy on the TWS, were also estimated. In view of the connotation of interactive variables, we suggested that spatiotemporal variations in TWS resulted from the coupling of the hydrological energy system, hydrological ecosystem, and hydrological system in the YRB, of which the hydrological system plays the most significant role, followed by the hydrological ecosystem.

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Socio-ecological determinants on spatio-temporal changes of groundwater in the Yellow River Basin, China

Cited by 28 publications

References 46 publications

Human activities are the key driver of water erosion changes in northeastern China

Human activities are the key driver of water erosion changes in northeastern China

Spatial correlation of groundwater level with natural factors using geographically weighted regression model in the Choushui River Alluvial Fan, Taiwan

Spatial Variations in Terrestrial Water Storage with Variable Forces across the Yellow River Basin

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