Validation of an In-Situ Observation Method for Nonpoint Source Pollution in Paddy Fields: A Case Study of a Beijing Paddy Field

A Beijing paddy field, along with in-situ experiments, was used to validate and refine the in-situ observation (IO) method to describe nonpoint source pollution (NPS) in paddy fields. Based on synchronous observed rainfall, water depth, and water quality data at two locations (1# (near inlet) and 2#...

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Autores principales: Ya Liao, Jingyi He, Baolin Su, Junfeng Dou, Yunqiang Xu, Lifen Li
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Publicado: MDPI AG 2021
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spelling oai:doaj.org-article:9e35157892bd448aba97abd57fa804c72021-11-25T19:15:49ZValidation of an In-Situ Observation Method for Nonpoint Source Pollution in Paddy Fields: A Case Study of a Beijing Paddy Field10.3390/w132232352073-4441https://doaj.org/article/9e35157892bd448aba97abd57fa804c72021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4441/13/22/3235https://doaj.org/toc/2073-4441A Beijing paddy field, along with in-situ experiments, was used to validate and refine the in-situ observation (IO) method to describe nonpoint source pollution (NPS) in paddy fields. Based on synchronous observed rainfall, water depth, and water quality data at two locations (1# (near inlet) and 2# (near outlet)) with large elevation differences, the evapotranspiration and infiltration loss (ET+F), runoff depth and NPS pollution load were calculated according to IO, and a common method was used to calculate ET+F. Then, the results of the different methods and locations were compared and analyzed. The results showed that 1# observation point was located at a lower position compared with 2# observation point. According to 1# observation point, there were 5 days of dry field in the drying period, which was consistent with the actual drying period, and there was a dry period of 9 days based on 2# observation point. The ET+F estimated by IO fit well with the calculated values. In the experiment, 6 overflows and 1 drainage event were identified from the observed data at locations 1# and 2#. The relative deviation of the NPS pollution of total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), nitrate-nitrogen (NO<sub>3</sub><sup>−</sup>-N) and ammonia nitrogen (NH<sub>4</sub><sup>+</sup>-N) was between 0.6% and 2.0%. The water level gauge location had little influence on IO but mostly affected the water depth observations during the field drying period. The mareographs should be installed in low-lying paddy field areas to monitor water depth variation throughout the whole rice-growing season.Ya LiaoJingyi HeBaolin SuJunfeng DouYunqiang XuLifen LiMDPI AGarticlepaddy fieldrainfallrunoffnon-point source pollutionin-situ methodHydraulic engineeringTC1-978Water supply for domestic and industrial purposesTD201-500ENWater, Vol 13, Iss 3235, p 3235 (2021)
institution DOAJ
collection DOAJ
language EN
topic paddy field
rainfall
runoff
non-point source pollution
in-situ method
Hydraulic engineering
TC1-978
Water supply for domestic and industrial purposes
TD201-500
spellingShingle paddy field
rainfall
runoff
non-point source pollution
in-situ method
Hydraulic engineering
TC1-978
Water supply for domestic and industrial purposes
TD201-500
Ya Liao
Jingyi He
Baolin Su
Junfeng Dou
Yunqiang Xu
Lifen Li
Validation of an In-Situ Observation Method for Nonpoint Source Pollution in Paddy Fields: A Case Study of a Beijing Paddy Field
description A Beijing paddy field, along with in-situ experiments, was used to validate and refine the in-situ observation (IO) method to describe nonpoint source pollution (NPS) in paddy fields. Based on synchronous observed rainfall, water depth, and water quality data at two locations (1# (near inlet) and 2# (near outlet)) with large elevation differences, the evapotranspiration and infiltration loss (ET+F), runoff depth and NPS pollution load were calculated according to IO, and a common method was used to calculate ET+F. Then, the results of the different methods and locations were compared and analyzed. The results showed that 1# observation point was located at a lower position compared with 2# observation point. According to 1# observation point, there were 5 days of dry field in the drying period, which was consistent with the actual drying period, and there was a dry period of 9 days based on 2# observation point. The ET+F estimated by IO fit well with the calculated values. In the experiment, 6 overflows and 1 drainage event were identified from the observed data at locations 1# and 2#. The relative deviation of the NPS pollution of total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), nitrate-nitrogen (NO<sub>3</sub><sup>−</sup>-N) and ammonia nitrogen (NH<sub>4</sub><sup>+</sup>-N) was between 0.6% and 2.0%. The water level gauge location had little influence on IO but mostly affected the water depth observations during the field drying period. The mareographs should be installed in low-lying paddy field areas to monitor water depth variation throughout the whole rice-growing season.
format article
author Ya Liao
Jingyi He
Baolin Su
Junfeng Dou
Yunqiang Xu
Lifen Li
author_facet Ya Liao
Jingyi He
Baolin Su
Junfeng Dou
Yunqiang Xu
Lifen Li
author_sort Ya Liao
title Validation of an In-Situ Observation Method for Nonpoint Source Pollution in Paddy Fields: A Case Study of a Beijing Paddy Field
title_short Validation of an In-Situ Observation Method for Nonpoint Source Pollution in Paddy Fields: A Case Study of a Beijing Paddy Field
title_full Validation of an In-Situ Observation Method for Nonpoint Source Pollution in Paddy Fields: A Case Study of a Beijing Paddy Field
title_fullStr Validation of an In-Situ Observation Method for Nonpoint Source Pollution in Paddy Fields: A Case Study of a Beijing Paddy Field
title_full_unstemmed Validation of an In-Situ Observation Method for Nonpoint Source Pollution in Paddy Fields: A Case Study of a Beijing Paddy Field
title_sort validation of an in-situ observation method for nonpoint source pollution in paddy fields: a case study of a beijing paddy field
publisher MDPI AG
publishDate 2021
url https://doaj.org/article/9e35157892bd448aba97abd57fa804c7
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