Impact of extreme rainfall on non-point source nitrogen loss in coastal basins of Laizhou Bay, China

Extreme rainfalls often lead to large amounts of nitrogen (N) loss from river basins. However, the composition and spatial variation of N loss caused by extreme events and the effects of control measures are not well understood. To shed light into this question, the Soil and Water Assessment Tool (S...
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Autor*in:	Jiang, Meng [verfasserIn] Peng, Hui [verfasserIn] Liang, Shengkang [verfasserIn] Wang, Shuo [verfasserIn] Kalin, Latif [verfasserIn] Baltaci, Enis [verfasserIn] Liu, Yang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Extreme rainfall Non-point source Nitrogen Slope LULC

Übergeordnetes Werk:	Enthalten in: The science of the total environment - Amsterdam [u.a.] : Elsevier Science, 1972, 881
Übergeordnetes Werk:	volume:881

DOI / URN:	10.1016/j.scitotenv.2023.163427

Katalog-ID:	ELV009952179

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245	1	0	\|a Impact of extreme rainfall on non-point source nitrogen loss in coastal basins of Laizhou Bay, China
264		1	\|c 2023
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520			\|a Extreme rainfalls often lead to large amounts of nitrogen (N) loss from river basins. However, the composition and spatial variation of N loss caused by extreme events and the effects of control measures are not well understood. To shed light into this question, the Soil and Water Assessment Tool (SWAT) was used to evaluate the spatiotemporal characteristics of organic and inorganic nitrogen (ON and IN) losses in the coastal basins of Laizhou Bay during typhoons Rumbia and Lekima. The effects of best management practices on controlling N loss were also explored during such extreme rainfall events. Results showed that extreme rainfall promoted transport of ON more than IN. The mass of ON and IN transported by the two typhoons exceeded 57 % and 39 % of the average annual N flux, respectively, and the loads were positively correlated with streamflow. During the two typhoons, the loss of ON was mainly concentrated in areas with steep slopes (θ > 15°) and natural vegetation (forests, grasslands, and shrublands). The IN loss was higher in areas with a 5–10° slope. Furthermore, subsurface flow was the main IN transport mechanism in areas with steep slope (θ > 5°). Simulations showed that implementation of filter strips in areas with slopes exceeding 10° can reduce N loss, with much greater reductions in ON (>36 %) than IN (>0.3 %). This study provides important insights into N loss during extreme events and the key role filter strips can play in trapping them before they reach downstream waterbodies.
650		4	\|a Extreme rainfall
650		4	\|a Non-point source
650		4	\|a Nitrogen
650		4	\|a Slope
650		4	\|a LULC
700	1		\|a Peng, Hui \|e verfasserin \|4 aut
700	1		\|a Liang, Shengkang \|e verfasserin \|4 aut
700	1		\|a Wang, Shuo \|e verfasserin \|4 aut
700	1		\|a Kalin, Latif \|e verfasserin \|4 aut
700	1		\|a Baltaci, Enis \|e verfasserin \|4 aut
700	1		\|a Liu, Yang \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t The science of the total environment \|d Amsterdam [u.a.] : Elsevier Science, 1972 \|g 881 \|h Online-Ressource \|w (DE-627)306591456 \|w (DE-600)1498726-0 \|w (DE-576)081953178 \|x 1879-1026 \|7 nnns
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912			\|a GBV_ILN_213
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912			\|a GBV_ILN_230
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
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912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
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912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
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912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
936	b	k	\|a 43.12 \|j Umweltchemie \|q VZ
936	b	k	\|a 43.13 \|j Umwelttoxikologie \|q VZ
936	b	k	\|a 44.13 \|j Medizinische Ökologie \|q VZ
951			\|a AR
952			\|d 881

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author_variant	m j mj h p hp s l sl s w sw l k lk e b eb y l yl
matchkey_str	article:18791026:2023----::matfxrmriflonnonsucntoelsicatl
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bklnumber	43.12 43.13 44.13
publishDate	2023
allfields	10.1016/j.scitotenv.2023.163427 doi (DE-627)ELV009952179 (ELSEVIER)S0048-9697(23)02046-6 DE-627 ger DE-627 rda eng 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Jiang, Meng verfasserin aut Impact of extreme rainfall on non-point source nitrogen loss in coastal basins of Laizhou Bay, China 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Extreme rainfalls often lead to large amounts of nitrogen (N) loss from river basins. However, the composition and spatial variation of N loss caused by extreme events and the effects of control measures are not well understood. To shed light into this question, the Soil and Water Assessment Tool (SWAT) was used to evaluate the spatiotemporal characteristics of organic and inorganic nitrogen (ON and IN) losses in the coastal basins of Laizhou Bay during typhoons Rumbia and Lekima. The effects of best management practices on controlling N loss were also explored during such extreme rainfall events. Results showed that extreme rainfall promoted transport of ON more than IN. The mass of ON and IN transported by the two typhoons exceeded 57 % and 39 % of the average annual N flux, respectively, and the loads were positively correlated with streamflow. During the two typhoons, the loss of ON was mainly concentrated in areas with steep slopes (θ > 15°) and natural vegetation (forests, grasslands, and shrublands). The IN loss was higher in areas with a 5–10° slope. Furthermore, subsurface flow was the main IN transport mechanism in areas with steep slope (θ > 5°). Simulations showed that implementation of filter strips in areas with slopes exceeding 10° can reduce N loss, with much greater reductions in ON (>36 %) than IN (>0.3 %). This study provides important insights into N loss during extreme events and the key role filter strips can play in trapping them before they reach downstream waterbodies. Extreme rainfall Non-point source Nitrogen Slope LULC Peng, Hui verfasserin aut Liang, Shengkang verfasserin aut Wang, Shuo verfasserin aut Kalin, Latif verfasserin aut Baltaci, Enis verfasserin aut Liu, Yang verfasserin aut Enthalten in The science of the total environment Amsterdam [u.a.] : Elsevier Science, 1972 881 Online-Ressource (DE-627)306591456 (DE-600)1498726-0 (DE-576)081953178 1879-1026 nnns volume:881 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 881
spelling	10.1016/j.scitotenv.2023.163427 doi (DE-627)ELV009952179 (ELSEVIER)S0048-9697(23)02046-6 DE-627 ger DE-627 rda eng 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Jiang, Meng verfasserin aut Impact of extreme rainfall on non-point source nitrogen loss in coastal basins of Laizhou Bay, China 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Extreme rainfalls often lead to large amounts of nitrogen (N) loss from river basins. However, the composition and spatial variation of N loss caused by extreme events and the effects of control measures are not well understood. To shed light into this question, the Soil and Water Assessment Tool (SWAT) was used to evaluate the spatiotemporal characteristics of organic and inorganic nitrogen (ON and IN) losses in the coastal basins of Laizhou Bay during typhoons Rumbia and Lekima. The effects of best management practices on controlling N loss were also explored during such extreme rainfall events. Results showed that extreme rainfall promoted transport of ON more than IN. The mass of ON and IN transported by the two typhoons exceeded 57 % and 39 % of the average annual N flux, respectively, and the loads were positively correlated with streamflow. During the two typhoons, the loss of ON was mainly concentrated in areas with steep slopes (θ > 15°) and natural vegetation (forests, grasslands, and shrublands). The IN loss was higher in areas with a 5–10° slope. Furthermore, subsurface flow was the main IN transport mechanism in areas with steep slope (θ > 5°). Simulations showed that implementation of filter strips in areas with slopes exceeding 10° can reduce N loss, with much greater reductions in ON (>36 %) than IN (>0.3 %). This study provides important insights into N loss during extreme events and the key role filter strips can play in trapping them before they reach downstream waterbodies. Extreme rainfall Non-point source Nitrogen Slope LULC Peng, Hui verfasserin aut Liang, Shengkang verfasserin aut Wang, Shuo verfasserin aut Kalin, Latif verfasserin aut Baltaci, Enis verfasserin aut Liu, Yang verfasserin aut Enthalten in The science of the total environment Amsterdam [u.a.] : Elsevier Science, 1972 881 Online-Ressource (DE-627)306591456 (DE-600)1498726-0 (DE-576)081953178 1879-1026 nnns volume:881 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 881
allfields_unstemmed	10.1016/j.scitotenv.2023.163427 doi (DE-627)ELV009952179 (ELSEVIER)S0048-9697(23)02046-6 DE-627 ger DE-627 rda eng 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Jiang, Meng verfasserin aut Impact of extreme rainfall on non-point source nitrogen loss in coastal basins of Laizhou Bay, China 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Extreme rainfalls often lead to large amounts of nitrogen (N) loss from river basins. However, the composition and spatial variation of N loss caused by extreme events and the effects of control measures are not well understood. To shed light into this question, the Soil and Water Assessment Tool (SWAT) was used to evaluate the spatiotemporal characteristics of organic and inorganic nitrogen (ON and IN) losses in the coastal basins of Laizhou Bay during typhoons Rumbia and Lekima. The effects of best management practices on controlling N loss were also explored during such extreme rainfall events. Results showed that extreme rainfall promoted transport of ON more than IN. The mass of ON and IN transported by the two typhoons exceeded 57 % and 39 % of the average annual N flux, respectively, and the loads were positively correlated with streamflow. During the two typhoons, the loss of ON was mainly concentrated in areas with steep slopes (θ > 15°) and natural vegetation (forests, grasslands, and shrublands). The IN loss was higher in areas with a 5–10° slope. Furthermore, subsurface flow was the main IN transport mechanism in areas with steep slope (θ > 5°). Simulations showed that implementation of filter strips in areas with slopes exceeding 10° can reduce N loss, with much greater reductions in ON (>36 %) than IN (>0.3 %). This study provides important insights into N loss during extreme events and the key role filter strips can play in trapping them before they reach downstream waterbodies. Extreme rainfall Non-point source Nitrogen Slope LULC Peng, Hui verfasserin aut Liang, Shengkang verfasserin aut Wang, Shuo verfasserin aut Kalin, Latif verfasserin aut Baltaci, Enis verfasserin aut Liu, Yang verfasserin aut Enthalten in The science of the total environment Amsterdam [u.a.] : Elsevier Science, 1972 881 Online-Ressource (DE-627)306591456 (DE-600)1498726-0 (DE-576)081953178 1879-1026 nnns volume:881 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 881
allfieldsGer	10.1016/j.scitotenv.2023.163427 doi (DE-627)ELV009952179 (ELSEVIER)S0048-9697(23)02046-6 DE-627 ger DE-627 rda eng 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Jiang, Meng verfasserin aut Impact of extreme rainfall on non-point source nitrogen loss in coastal basins of Laizhou Bay, China 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Extreme rainfalls often lead to large amounts of nitrogen (N) loss from river basins. However, the composition and spatial variation of N loss caused by extreme events and the effects of control measures are not well understood. To shed light into this question, the Soil and Water Assessment Tool (SWAT) was used to evaluate the spatiotemporal characteristics of organic and inorganic nitrogen (ON and IN) losses in the coastal basins of Laizhou Bay during typhoons Rumbia and Lekima. The effects of best management practices on controlling N loss were also explored during such extreme rainfall events. Results showed that extreme rainfall promoted transport of ON more than IN. The mass of ON and IN transported by the two typhoons exceeded 57 % and 39 % of the average annual N flux, respectively, and the loads were positively correlated with streamflow. During the two typhoons, the loss of ON was mainly concentrated in areas with steep slopes (θ > 15°) and natural vegetation (forests, grasslands, and shrublands). The IN loss was higher in areas with a 5–10° slope. Furthermore, subsurface flow was the main IN transport mechanism in areas with steep slope (θ > 5°). Simulations showed that implementation of filter strips in areas with slopes exceeding 10° can reduce N loss, with much greater reductions in ON (>36 %) than IN (>0.3 %). This study provides important insights into N loss during extreme events and the key role filter strips can play in trapping them before they reach downstream waterbodies. Extreme rainfall Non-point source Nitrogen Slope LULC Peng, Hui verfasserin aut Liang, Shengkang verfasserin aut Wang, Shuo verfasserin aut Kalin, Latif verfasserin aut Baltaci, Enis verfasserin aut Liu, Yang verfasserin aut Enthalten in The science of the total environment Amsterdam [u.a.] : Elsevier Science, 1972 881 Online-Ressource (DE-627)306591456 (DE-600)1498726-0 (DE-576)081953178 1879-1026 nnns volume:881 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 881
allfieldsSound	10.1016/j.scitotenv.2023.163427 doi (DE-627)ELV009952179 (ELSEVIER)S0048-9697(23)02046-6 DE-627 ger DE-627 rda eng 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Jiang, Meng verfasserin aut Impact of extreme rainfall on non-point source nitrogen loss in coastal basins of Laizhou Bay, China 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Extreme rainfalls often lead to large amounts of nitrogen (N) loss from river basins. However, the composition and spatial variation of N loss caused by extreme events and the effects of control measures are not well understood. To shed light into this question, the Soil and Water Assessment Tool (SWAT) was used to evaluate the spatiotemporal characteristics of organic and inorganic nitrogen (ON and IN) losses in the coastal basins of Laizhou Bay during typhoons Rumbia and Lekima. The effects of best management practices on controlling N loss were also explored during such extreme rainfall events. Results showed that extreme rainfall promoted transport of ON more than IN. The mass of ON and IN transported by the two typhoons exceeded 57 % and 39 % of the average annual N flux, respectively, and the loads were positively correlated with streamflow. During the two typhoons, the loss of ON was mainly concentrated in areas with steep slopes (θ > 15°) and natural vegetation (forests, grasslands, and shrublands). The IN loss was higher in areas with a 5–10° slope. Furthermore, subsurface flow was the main IN transport mechanism in areas with steep slope (θ > 5°). Simulations showed that implementation of filter strips in areas with slopes exceeding 10° can reduce N loss, with much greater reductions in ON (>36 %) than IN (>0.3 %). This study provides important insights into N loss during extreme events and the key role filter strips can play in trapping them before they reach downstream waterbodies. Extreme rainfall Non-point source Nitrogen Slope LULC Peng, Hui verfasserin aut Liang, Shengkang verfasserin aut Wang, Shuo verfasserin aut Kalin, Latif verfasserin aut Baltaci, Enis verfasserin aut Liu, Yang verfasserin aut Enthalten in The science of the total environment Amsterdam [u.a.] : Elsevier Science, 1972 881 Online-Ressource (DE-627)306591456 (DE-600)1498726-0 (DE-576)081953178 1879-1026 nnns volume:881 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 881
language	English
source	Enthalten in The science of the total environment 881 volume:881
sourceStr	Enthalten in The science of the total environment 881 volume:881
format_phy_str_mv	Article
bklname	Umweltchemie Umwelttoxikologie Medizinische Ökologie
institution	findex.gbv.de
topic_facet	Extreme rainfall Non-point source Nitrogen Slope LULC
dewey-raw	333.7
isfreeaccess_bool	false
container_title	The science of the total environment
authorswithroles_txt_mv	Jiang, Meng @@aut@@ Peng, Hui @@aut@@ Liang, Shengkang @@aut@@ Wang, Shuo @@aut@@ Kalin, Latif @@aut@@ Baltaci, Enis @@aut@@ Liu, Yang @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	306591456
dewey-sort	3333.7
id	ELV009952179
language_de	englisch
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author	Jiang, Meng
spellingShingle	Jiang, Meng ddc 333.7 bkl 43.12 bkl 43.13 bkl 44.13 misc Extreme rainfall misc Non-point source misc Nitrogen misc Slope misc LULC Impact of extreme rainfall on non-point source nitrogen loss in coastal basins of Laizhou Bay, China
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topic_title	333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Impact of extreme rainfall on non-point source nitrogen loss in coastal basins of Laizhou Bay, China Extreme rainfall Non-point source Nitrogen Slope LULC
topic	ddc 333.7 bkl 43.12 bkl 43.13 bkl 44.13 misc Extreme rainfall misc Non-point source misc Nitrogen misc Slope misc LULC
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title	Impact of extreme rainfall on non-point source nitrogen loss in coastal basins of Laizhou Bay, China
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title_full	Impact of extreme rainfall on non-point source nitrogen loss in coastal basins of Laizhou Bay, China
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title_sort	impact of extreme rainfall on non-point source nitrogen loss in coastal basins of laizhou bay, china
title_auth	Impact of extreme rainfall on non-point source nitrogen loss in coastal basins of Laizhou Bay, China
abstract	Extreme rainfalls often lead to large amounts of nitrogen (N) loss from river basins. However, the composition and spatial variation of N loss caused by extreme events and the effects of control measures are not well understood. To shed light into this question, the Soil and Water Assessment Tool (SWAT) was used to evaluate the spatiotemporal characteristics of organic and inorganic nitrogen (ON and IN) losses in the coastal basins of Laizhou Bay during typhoons Rumbia and Lekima. The effects of best management practices on controlling N loss were also explored during such extreme rainfall events. Results showed that extreme rainfall promoted transport of ON more than IN. The mass of ON and IN transported by the two typhoons exceeded 57 % and 39 % of the average annual N flux, respectively, and the loads were positively correlated with streamflow. During the two typhoons, the loss of ON was mainly concentrated in areas with steep slopes (θ > 15°) and natural vegetation (forests, grasslands, and shrublands). The IN loss was higher in areas with a 5–10° slope. Furthermore, subsurface flow was the main IN transport mechanism in areas with steep slope (θ > 5°). Simulations showed that implementation of filter strips in areas with slopes exceeding 10° can reduce N loss, with much greater reductions in ON (>36 %) than IN (>0.3 %). This study provides important insights into N loss during extreme events and the key role filter strips can play in trapping them before they reach downstream waterbodies.
abstractGer	Extreme rainfalls often lead to large amounts of nitrogen (N) loss from river basins. However, the composition and spatial variation of N loss caused by extreme events and the effects of control measures are not well understood. To shed light into this question, the Soil and Water Assessment Tool (SWAT) was used to evaluate the spatiotemporal characteristics of organic and inorganic nitrogen (ON and IN) losses in the coastal basins of Laizhou Bay during typhoons Rumbia and Lekima. The effects of best management practices on controlling N loss were also explored during such extreme rainfall events. Results showed that extreme rainfall promoted transport of ON more than IN. The mass of ON and IN transported by the two typhoons exceeded 57 % and 39 % of the average annual N flux, respectively, and the loads were positively correlated with streamflow. During the two typhoons, the loss of ON was mainly concentrated in areas with steep slopes (θ > 15°) and natural vegetation (forests, grasslands, and shrublands). The IN loss was higher in areas with a 5–10° slope. Furthermore, subsurface flow was the main IN transport mechanism in areas with steep slope (θ > 5°). Simulations showed that implementation of filter strips in areas with slopes exceeding 10° can reduce N loss, with much greater reductions in ON (>36 %) than IN (>0.3 %). This study provides important insights into N loss during extreme events and the key role filter strips can play in trapping them before they reach downstream waterbodies.
abstract_unstemmed	Extreme rainfalls often lead to large amounts of nitrogen (N) loss from river basins. However, the composition and spatial variation of N loss caused by extreme events and the effects of control measures are not well understood. To shed light into this question, the Soil and Water Assessment Tool (SWAT) was used to evaluate the spatiotemporal characteristics of organic and inorganic nitrogen (ON and IN) losses in the coastal basins of Laizhou Bay during typhoons Rumbia and Lekima. The effects of best management practices on controlling N loss were also explored during such extreme rainfall events. Results showed that extreme rainfall promoted transport of ON more than IN. The mass of ON and IN transported by the two typhoons exceeded 57 % and 39 % of the average annual N flux, respectively, and the loads were positively correlated with streamflow. During the two typhoons, the loss of ON was mainly concentrated in areas with steep slopes (θ > 15°) and natural vegetation (forests, grasslands, and shrublands). The IN loss was higher in areas with a 5–10° slope. Furthermore, subsurface flow was the main IN transport mechanism in areas with steep slope (θ > 5°). Simulations showed that implementation of filter strips in areas with slopes exceeding 10° can reduce N loss, with much greater reductions in ON (>36 %) than IN (>0.3 %). This study provides important insights into N loss during extreme events and the key role filter strips can play in trapping them before they reach downstream waterbodies.
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title_short	Impact of extreme rainfall on non-point source nitrogen loss in coastal basins of Laizhou Bay, China
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author2	Peng, Hui Liang, Shengkang Wang, Shuo Kalin, Latif Baltaci, Enis Liu, Yang
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However, the composition and spatial variation of N loss caused by extreme events and the effects of control measures are not well understood. To shed light into this question, the Soil and Water Assessment Tool (SWAT) was used to evaluate the spatiotemporal characteristics of organic and inorganic nitrogen (ON and IN) losses in the coastal basins of Laizhou Bay during typhoons Rumbia and Lekima. The effects of best management practices on controlling N loss were also explored during such extreme rainfall events. Results showed that extreme rainfall promoted transport of ON more than IN. The mass of ON and IN transported by the two typhoons exceeded 57 % and 39 % of the average annual N flux, respectively, and the loads were positively correlated with streamflow. During the two typhoons, the loss of ON was mainly concentrated in areas with steep slopes (θ > 15°) and natural vegetation (forests, grasslands, and shrublands). The IN loss was higher in areas with a 5–10° slope. Furthermore, subsurface flow was the main IN transport mechanism in areas with steep slope (θ > 5°). Simulations showed that implementation of filter strips in areas with slopes exceeding 10° can reduce N loss, with much greater reductions in ON (>36 %) than IN (>0.3 %). 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Impact of extreme rainfall on non-point source nitrogen loss in coastal basins of Laizhou Bay, China

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