Evaluating the spatial scaling effect of baseflow and baseflow nonpoint source pollution in a nested watershed

Zhu, Yingxin; Lei, Chen; Wang, Kai; Wang, Wenzhuo; Wang, Yuming; Shen, Zhenyao

doi:10.1016/j.jhydrol.2019.124221

Cited by 17 publications

(2 citation statements)

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“…It is worth noting that the weak explanatory power of elevation observed in this study may be attributed to the influence of spatial scale effects on identifying influential factors [65,66]. Shen et al [31] reported elevation as the predominant driver of spatial variance in the nutrient budget within the TGRA.…”

Section: Cost-effectiveness Of Bmp Scenariosmentioning

confidence: 62%

Optimizing Non-Point Source Pollution Management: Evaluating Cost-Effective Strategies in a Small Watershed within the Three Gorges Reservoir Area, China

Chang,

Wang,

Liu

et al. 2024

Land

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Non-point source (NPS) pollution poses a significant threat to the water environment, yet controlling it at the watershed scale remains a formidable challenge. Understanding the characteristics and drivers of nitrogen (N) and phosphorus (P) outputs at the watershed scale, along with identifying cost-effective best management practices (BMPs), is crucial for effective pollution control. In this study, we utilized the Wangjiaqiao watershed within the Three Gorges Reservoir Area (TGRA) as a case study to explore the characteristics of N and P load outputs and their dominant drivers by combining the SWAT model and a geographic detector. Based on our analysis of N and P loads within the watershed, we employed the entropy weight method to evaluate the reduction efficiency and cost-effectiveness of 64 BMP scenarios, encompassing seven measures (vegetative filter strips, parallel terraces, 10% fertilizer reduction, 30% fertilizer reduction, residue cover tillage, grass mulching, and returning farmland to forest) and their combinations. Our findings revealed the following: (1) spatial heterogeneity in NPS loads within the watershed, primarily influenced by land use, fertilizer application, and surface runoff, with interactive enhancement effects among driving factors; (2) the differential effectiveness of BMPs at the watershed level, with structural measures, particularly terracing, exhibiting higher efficacy and achieving reduction rates of 28.12% for total nitrogen (TN) and 37.69% for total phosphorus (TP); the combined BMPs showed improved reduction efficiency, but not merely additive; and (3) in terms of cost-effectiveness, 30% fertilizer reduction emerged as the most beneficial among the individual measures. Moreover, a combination of vegetative filter strips, parallel terraces, and 30% fertilizer reduction demonstrated significant improvements in TN and TP reductions (48.05% and 61.95%, respectively), suggesting their widespread applicability. Overall, our study provides insights into developing a cost-effective BMP strategy for the Wangjiaqiao watershed and offers valuable guidance for NPS pollution management in similar small watersheds within the TGRA.

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Section: Cost-effectiveness Of Bmp Scenariosmentioning

confidence: 62%

Optimizing Non-Point Source Pollution Management: Evaluating Cost-Effective Strategies in a Small Watershed within the Three Gorges Reservoir Area, China

Chang,

Wang,

Liu

et al. 2024

Land

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“…It not only plays a critical role in maintaining the basic ecological function of a river (Shao et al, 2020; Stoelzle et al, 2020), ensuring access to safe water (Halford & Mayer, 2000) in the dry season, but is also a vital component of river runoff during the high‐flow period and storm runoff process (Donn et al, 2012). Baseflow quantification is one of the most common tasks in water resource assessment (Chen & Teegavarapu, 2020; Rumsey et al, 2015) and optimal allocation (Longobardi et al, 2016), non‐point source pollution quantification (Fisher et al, 2010; He & Lu, 2021; Schilling & Zhang, 2004; Zhu et al, 2019), control of river algal blooms and salinity (Lemley et al, 2020; Santhi et al, 2008), and watershed hydrological and water quality modelling (Zhang et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Impacts of parameter uncertainty on baseflow separation by a two‐parameter recursive digital filter

Tang

et al. 2022

Hydrological Processes

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Accurate baseflow quantification is an important step in the survey and evaluation of water resources, non‐point source pollution quantification, and optimization of water resource allocation. We selected a typical agricultural watershed with high precipitation rates in eastern China as the study area and investigated the impact of the combination of a recession constant (on an annual, monthly, or daily scale) and the maximum baseflow index (BFImax; empirical value vs. optimal value) on the accuracy and reliability of baseflow separated using a two‐parameter recursive digital filter (TPRDF). It was observed that the accuracy and reliability of the TPRDF‐separated baseflow varied with the time scale of the recession constant. Specifically, they decreased in the following order: baseflow estimated using daily recession constants (EBSD) (NSE = 0.797, R2 = 0.819) > baseflow estimated using monthly recession constants (EBSM; NSE = 0.779, R2 = 0.801) > baseflow estimated using annual recession constants (EBSY; NSE = 0.666, R2 = 0.675), where ‘NSE’ is the Nash–Sutcliffe efficiency coefficient. The empirical value setting of BFImax was another important reason for the uncertainties in the TPRDF‐separated baseflow. The NSE values of EBSY, EBSM, and EBSD increased from 0.666, 0.779, and 0.797–0.785, 0.859, and 0.876, respectively, after replacing the empirical values with numerically optimized values. The combination of BFImax with recession constants on various time scales significantly affected the annual total TPRDF‐separated baseflow. It also affected the overall variation characteristics of the baseflow time series, composition of different magnitude (frequency) components, and sensitivity of baseflow time series to rainfall events.

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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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Evaluating the spatial scaling effect of baseflow and baseflow nonpoint source pollution in a nested watershed

Cited by 17 publications

References 50 publications

Optimizing Non-Point Source Pollution Management: Evaluating Cost-Effective Strategies in a Small Watershed within the Three Gorges Reservoir Area, China

Optimizing Non-Point Source Pollution Management: Evaluating Cost-Effective Strategies in a Small Watershed within the Three Gorges Reservoir Area, China

Impacts of parameter uncertainty on baseflow separation by a two‐parameter recursive digital filter

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

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