Identifying Surface Runoff Pathways for Cost-Effective Mitigation of Pollutant Inputs to Drinking Water Reservoir

Abstract

Surface runoff (overland flow) is the main element of the water cycle and is also crucial in the delivery of phosphorus and nitrogen from catchments to water bodies. Watercourses and reservoirs in agricultural catchments are particularly vulnerable to the delivery of biogenic compounds via surface runoff. Forested riparian buffers are considered effective in reducing nutrients and sediment loads in runoff from agricultural areas. Regrettably, the concentration of surface runoff may significantly limit the buffering capacity of vegetation strips, as channelised overland flow tends to avoid buffers without making optimal use of their ability to retain nutrients and sediment. The aim of the undertaken research was to delineate surface runoff pathways from surrounding areas to a drinking water reservoir as well as to identify potential concentration spots of overland flow. The research was conducted for the Dobromierz drinking water reservoir (GPS N: 50 • 54 27", E: 16 • 14 37"). The reservoir is situated in a submountain catchment, where rainfall is an important factor taking part in driving diffuse P and N loads from land to water. Presented GIS-based method using high resolution Digital Terrain Model obtained from Light Detection and Ranging (LiDAR) allowed to determine areas with a tendency for high accumulation (concentration) of overland flow in the direct catchment of the reservoir. As main surface runoff areas, three sites each exceeding 100 ha were designated. The analysis of spatial data also allowed to establish the risk of agricultural diffuse pollution transfer via channelised overland flow to the reservoir from individual accumulation areas. It was found that in the forested part of the catchment (serving as a riparian buffer) there is no visible tendency for concentration of surface runoff, but simultaneously the vegetation strip does not prevent the transfer of runoff waters from agricultural areas through the privileged pathways of concentrated flow.

Influencing Factors Analysis of Taiwan Eutrophicated Reservoirs

“…Correlation of chlorophyll-a (Chl-a) abundance with eutrophication has frequently been investigated using the integration approach of multivariate statistical analysis [4,5]. According to several studies, increased temperature, total precipitation and nutrient runoff are the main factors that enhances the eutrophication process in nature [5][6][7][8]. The nutrient factor is an important factor for predicting the eutrophic level.…”

DTM resolution controls the accuracy of estimating surface runoff indicators in flat, lowland landscapes

“…Smith et al, 2001; R. Smith et al, 2015) and contaminant transport (e.g., Chrétien et al, 2017; Coyne et al, 1995; Peyton et al, 2016; Rakonjac et al, 2022) from agricultural fields to surface waters. In this capacity, surface runoff has been linked to eutrophication and algal blooms (Dolph et al, 2019; Sharpley et al, 1994; Wurtsbaugh et al, 2019), loss of biodiversity (Dudgeon, 2019; Leip et al, 2015) and drinking water pollution (Dąbrowska et al, 2018; Gilliom, 2007; Kool et al, 2023). As surface runoff also leads to soil erosion and contributes to peak discharges in streams and rivers, there is a need for scientists and water managers to assess its occurrence, magnitude, and relative contribution to nutrient and contaminant transport (Massop et al, 2017; van der Velde et al, 2010; Worm et al, 2019).…”

Long-Term Impact of Wind Erosion on the Particle Size Distribution of Soils in the Eastern Part of the European Union

“…Research related to soil and land management and environmental decision support systems is nowadays facilitated by increasing availability of high-resolution spatial data, remote sensing data and GIS analysis tools. Progressive climate change makes such studies a pressing need [50][51][52][53][54][55]. Current wind erosion models provide detailed knowledge necessary to predict and monitor this phenomenon and to implement effective mitigation measures, and are developed at different temporal and spatial scales using data integration from multiple sources [4,6,56,57].…”