2016
DOI: 10.1007/s10333-016-0546-y
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Influence of the agricultural land agglomeration to the nutrients of the river water in the Tokachi River basin

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Cited by 4 publications
(10 citation statements)
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“…Based on the TN and TP indicators, there is a potential risk of eutrophication incidence provided there is coincidence of other relevant factors (temperature, chlorophyll, water transparency, oxygen level, etc.). Similar concentrations of N and P, their ranges and contribution of individual forms were reported in other studies on this issue [55,58,90].…”
Section: Nutrient Status In the Canal Networksupporting
confidence: 86%
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“…Based on the TN and TP indicators, there is a potential risk of eutrophication incidence provided there is coincidence of other relevant factors (temperature, chlorophyll, water transparency, oxygen level, etc.). Similar concentrations of N and P, their ranges and contribution of individual forms were reported in other studies on this issue [55,58,90].…”
Section: Nutrient Status In the Canal Networksupporting
confidence: 86%
“…Similar results of N and P distribution in surface water were reported in other studies [16,21,35,39,56,75,80,91]. Leaching and runoff of nutrients from the surrounding farmlands after heavy precipitation, or inflow of water from the drainage system, can impact the seasonal distribution of nutrients in a waterbody as well [1,7,22,55,58,75].…”
Section: Tn:tp Ratiosupporting
confidence: 80%
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“…Such data sets also indicate long-term changes in catchment responses and may contain evidence of rare events (e.g., severe droughts or floods) or rapid shifts in the system's behaviour [43][44][45]. Globally, studies have made use of long-term data in order to assess changes in river water quality including nitrate fluxes [46], hydrology [38], the effects of agriculture [47], the impact of reservoir construction on downstream flow regime [9] and the effects of land use changes [48]. In addition, the use of multivariate approaches and water quality indices, such as the Physicochemical Driver Assessment Index (PAI), allows for the interpretation of complex data sets in order to better understand the quality and ecological status of a studied ecosystem [3].…”
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confidence: 99%