Modelling the sources and transport of ammonium nitrogen with the SPARROW model: A case study in a karst basin

Dai, Yibin; Lang, Yunchao; Wang, Zhihui; Han, Xiaokun; Wang, Lichun; Zhong, Jun

doi:10.1016/j.jhydrol.2020.125763

Cited by 21 publications

(8 citation statements)

References 58 publications

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“…The TN and TP concentrations in the WRB ranged from 0.25 to 5.72 mg/L and 0.02 to 1.31 mg/L, with a mean concentration of 3.83 mg/L and 0.56 mg/L respectively. The central and southeast portions of WRB are the most contaminated, with significant amounts of TN and TP, which is consistent with the spatial distribution of agricultural non-point source losses documented in this watershed (Dai et al, 2021;Xu et al, 2021a).…”

Section: Assessment Of Water Quality Impairments In Wrbsupporting

confidence: 82%

“…The grid soil permeability data was computed by the Python-ROSSETA model according to soil texture (Zhang et al, 2018). The average annual streamflow of the reaches is modeled based on the water balance Budyko model (Zhang et al, 2004) through annual predication and potential evapotranspiration, and all the parameters are participated in calculation referred to (Dai et al, 2021). The landscape index is calculated from Fragstats 4.3.…”

Section: Meteorological and Landscape Data Sources And Data Pre-proce...mentioning

confidence: 99%

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Decoding river pollution trends and their landscape determinants in an ecologically fragile karst basin using a machine learning model

Fan

Oliver

et al. 2022

Environmental Research

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Section: Assessment Of Water Quality Impairments In Wrbsupporting

confidence: 82%

Section: Meteorological and Landscape Data Sources And Data Pre-proce...mentioning

confidence: 99%

Decoding river pollution trends and their landscape determinants in an ecologically fragile karst basin using a machine learning model

Fan

Oliver

et al. 2022

Environmental Research

Self Cite

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“…When there is heavy rainfall, there is no abrupt decrease in the NH 4+ -N concentration in all layers of the different karst landforms. Only when there is a higher soil moisture content does NH 4+ -N redistribute and migrate to accumulate in the deeper layers of the soil, resulting in a decrease in NH 4+ -N concentration in the upper layers and an increase in the lower layers (Dai et al 2021). In the non-irrigated paddy elds, at the 30 cm depth, NO 3− -N concentrations showed an increasing trend in both non-karst landforms and the peak forest plain but a decreasing trend in the sinkhole area.…”

Section: Discussionmentioning

confidence: 99%

The impact of karst landform types and land uses on the leaching of nitrogen and phosphorus in soil solution

Gong,

Han,

Dai

et al. 2023

Preprint

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To analyze the impact of karst landform types and land use patterns on the movement of nitrogen and phosphorus in soil solution, we utilized soil solution nitrogen and phosphorus monitoring data from the Huixian Wetland Experimental Area in the Li River Basin from March to December 2021. The study area was divided into three categories based on karst landform types, namely, non-karst landforms, peak-cluster depressions, and peak-forest plains. Additionally, two categories were considered based on land use patterns, namely, paddy fields and uplands. Single-factor variance analysis and Pearson correlation analysis were employed to examine the transportation of soil solution nitrogen and phosphorus at different soil depths during the irrigation and non-irrigation seasons. The results indicate that (1) the concentrations of NO3--N in the soil solution at depths of 30 cm, 60 cm, and 100 cm exhibited significant differences between the irrigation and non-irrigation seasons. The total nitrogen (TN) concentration at a depth of 30 cm also displayed significant differences between the two seasons. Pearson correlation analysis of nitrogen and phosphorus concentrations in the soil solution revealed a significant positive correlation between NO3--N and TN, with a correlation coefficient of 0.666 (p<0.01). (2) The downward migration distance of NO3--N varied among different karst landform types, following the sequence of peak-forest plains > peak-cluster depressions > non-karst landforms. The NH4+-N migration distance followed the order of peak-cluster depressions > peak-forest plains > non-karst landforms. (3) The NO3--N concentration in irrigated paddy fields generally remained at a high level at a depth of 100 cm during the irrigation season. In upland areas, the NO3--N concentration followed a consistent pattern of 30 cm > 60 cm > 100 cm. However, during periods of heavy rainfall, anomalous increases or decreases in concentration were observed at depths of 60 cm and 100 cm.

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“…SWAT simulates transport using hydrologic response units, which lump all similar land uses, soils, and slopes within a subbasin. Hybrid empirical and process-based models such as the SPAtially Referenced Regression On Watershed attributes (SPARROW), a GIS-based watershed model developed by the United States Geological Survey (USGS), uses a hybrid approach to estimate nutrient sources, transport, and loadings around the world. ,− In summary, existing models (i.e., SWAT, SPARROW) consider nutrient sources and retention on landscape and stream networks to predict nutrient and source contributions at the basin/watershed scale. For instance, SPARROW predicts nutrient loadings and sources for tributaries with an area higher than 150 km 2 and further provides the ranking of sub-basins within these tributaries .…”

Section: Introductionmentioning

confidence: 99%

Important Role of Overland Flows and Tile Field Pathways in Nutrient Transport

Wan,

Kendall,

Martin

et al. 2023

Environ. Sci. Technol.

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Nitrogen and phosphorus pollution is of great concern to aquatic life and human well-being. While most of these nutrients are applied to the landscape, little is known about the complex interplay among nutrient applications, transport attenuation processes, and coastal loads. Here, we enhance and apply the Spatially Explicit Nutrient Source Estimate and Flux model (SENSEflux) to simulate the total annual nitrogen and phosphorus loads from the US Great Lakes Basin to the coastline, identify nutrient delivery hotspots, and estimate the relative contributions of different sources and pathways at a high resolution (120 m). In addition to in-stream uptake, the main novelty of this model is that SENSEflux explicitly describes nutrient attenuation through four distinct pathways that are seldom described jointly in other models: runoff from tile-drained agricultural fields, overland runoff, groundwater flow, and septic plumes within groundwater. Our analysis shows that agricultural sources are dominant for both total nitrogen (TN) (58%) and total phosphorus (TP) (46%) deliveries to the Great Lakes. In addition, this study reveals that the surface pathways (sum of overland flow and tile field drainage) dominate nutrient delivery, transporting 66% of the TN and 76% of the TP loads to the US Great Lakes coastline. Importantly, this study provides the first basin-wide estimates of both nonseptic groundwater (TN: 26%; TP: 5%) and septic-plume groundwater (TN: 4%; TP: 2%) deliveries of nutrients to the lakes. This work provides valuable information for environmental managers to target efforts to reduce nutrient loads to the Great Lakes, which could be transferred to other regions worldwide that are facing similar nutrient management challenges.

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Modelling the sources and transport of ammonium nitrogen with the SPARROW model: A case study in a karst basin

Cited by 21 publications

References 58 publications

Decoding river pollution trends and their landscape determinants in an ecologically fragile karst basin using a machine learning model

Decoding river pollution trends and their landscape determinants in an ecologically fragile karst basin using a machine learning model

The impact of karst landform types and land uses on the leaching of nitrogen and phosphorus in soil solution

Important Role of Overland Flows and Tile Field Pathways in Nutrient Transport

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