Spatiotemporal changes of nitrate retention at the interface between surface water and groundwater: Insight from watershed scale in an elevated nitrate region

Abstract

Understanding the spatiotemporal nitrate retention in streambed is essential for developing management practices in reducing nitrate enrichment. However，the process of nitrate change in the profile of streambed at an elevated nitrate across a watershed scale is still not sufficiently investigated. In this study, we used a combination of hydraulic and hydro‐geochemistry methods to quantify total nitrate retention in streambeds of an agriculture‐intensive watershed in Central China. To conduct surface and groundwater measurements, we collected 1440 water samples for nitrate analysis from 40 shallow drilled wells during the dry and wet seasons from 2018 to 2020. The results showed a clear spatiotemporal variation of nitrate retention in streambed in the watershed. Spatially, nitrate retention in the midstream and downstream reaches was higher than that of the upstream reach. The lowest point of nitrate retention in downstream both in dry and wet seasons was at the depth of 0.75 m. While the lowest nitrate retention was found in midstream and upstream reaches, both in the dry and wet seasons at the depth between 1.5 and 2.5 m. Temporally, nitrate retention was higher in the wet season (1.56 μg N m−2d−1) than in dry season (1.41 μg N m−2d−1). DO min at 3 mg/L was found to the nitrate retention zero threshold in up and midstream. Water change fluxes and nitrate retention both have positive and negative relationship at watershed scale. Nitrate retention at the watershed scale was strongly affected by streambed lithology, precipitation, surface water ‐ groundwater exchange, and human activities. Those findings can provide reference for nitrate removal in international important agricultural areas.

Groundwater quality assessment using water quality index and principal component analysis in the Achnera block, Agra district, Uttar Pradesh, Northern India

“…Therefore, it is advised that the drinking water should be treated before making it suitable for drinking in Achhnera block of Agra region, Northern India. It can be concluded from the comparative analysis that potable water of the rural areas is comparatively more polluted than that of the urban areas, which may be due to geogenic as well as anthropogenic activities (use of fluoride laden fertilizer in the field, leeches into the groundwater) 13 – 15 .…”

“…It has been reported in previous literatures that, contaminants like heavy metals, pesticides, organic and inorganic pollutant are causing the serious human health disease such as hypertension, hypocalcaemia, kidney stones, gastro-renal discomfort, arterial calcification, thrombosis 8 – 12 . Apart from the availability of heavy metals in drinking water, the presence of nitrogen has also been proven as the strong potential threat to the quality of drinking water 13 – 15 . With the increasing groundwater pollution, it is essential to analyze groundwater chemical characteristics and evaluate groundwater quality for water supply purpose.…”

A new data-driven model to predict monthly runoff at watershed scale: insights from deep learning method applied in data-driven model

“…The basin belongs to warm temperate continental monsoon climate, the average annual temperature is 7.8 ~ 13.9 °C, the average annual rainfall is 710 ~ 930 mm, and the rainfall is concentrated in June to September. The lower reaches of the Yiluo River are one of the largest wheat production bases in China, with concentrated urban distribution and frequent industrial and agricultural activities (Wang et al, 2021;Wang et al, 2023).…”