Measuring the zonal responses of nitrogen output to landscape pattern in a flatland with river network: a case study in Taihu Lake Basin, China
Abstract Landscape pattern changes induced by rapid urbanization and intensified agricultural activities have exerted great pressure on regional water purification services. Relationship between landscape metrics and nitrogen-related ecosystem services has been a major concern of many scholars and h...
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| Autor*in: |
Wang, Ya’nan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Environmental science and pollution research - Berlin : Springer, 1994, 29(2022), 23 vom: 18. Jan., Seite 34624-34636 |
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| Übergeordnetes Werk: |
volume:29 ; year:2022 ; number:23 ; day:18 ; month:01 ; pages:34624-34636 |
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| DOI / URN: |
10.1007/s11356-021-15842-x |
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| Katalog-ID: |
SPR046928472 |
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| 100 | 1 | |a Wang, Ya’nan |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Measuring the zonal responses of nitrogen output to landscape pattern in a flatland with river network: a case study in Taihu Lake Basin, China |
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| 520 | |a Abstract Landscape pattern changes induced by rapid urbanization and intensified agricultural activities have exerted great pressure on regional water purification services. Relationship between landscape metrics and nitrogen-related ecosystem services has been a major concern of many scholars and has been widely used for guidance for land use and cover (LULC) management. However, clear zonal differences may exist, especially in highly developed reticular river network area, thus limiting our understanding of nitrogen output (NOP) to landscape pattern in the details. The spatial distribution of regional NOP was obtained based on the InVEST model. The zonal responses of NOP to landscape patter were examined under hydraulic subregions and subbasin scale. The results show that the unit value of average NOP in the Taihu Lake Basin (TLB) was 146.14 (kg/$ km^{2} $), and the total output reached 23677.92 t in 2020. The simulation NOP showed reasonable agreement with verified water quality observations in the lake inlet stations, with an R2 of 0.76. In terms of space composition, merely cropland have significant effects on NOP in the whole basin scale, while the explanatory variables include cropland and developed land in Pudong (PD), Puxi (PX), Wuchengxiyu (WC), and Hangjiahu (HJ) regions. In Huxi (HX) and Yangchengdianmao (YC) regions, cropland and forest are the significant impact types, while in (Zhexi) ZX region, cropland, developed land, and forest are significant impact types. In the space configuration, the percentage of landscape (PLAND) or largest patch index (LPI) of cropland showed positive effects about NOP, whether in the whole basin or the hydraulic subregions. Edge density (ED) (−3.48), number of patches (NP) (−3.91), and percentage of like adjacencies (PLAND) (−2.80) of the forest exhibit negative correlations with NOP, in the HX, ZX, and YC region, respectively. It displays diversiform in the response of NOP to the landscape metric of developed land, which speculate that the heterogeneity of developed land can also have a constraint on NOP, in the highly urbanized areas with less forest area. In addition, the total nitrogen output of the TLB needs to be controlled, especially in HJ region which was identified as the sensitive area of pollution sources with the largest NOP and should be paid more attention to. Compared with the administrative management unit, it is more reasonable to control and manage the pollution sources by referring to the hydraulic subregions and subbasin units. Senior managers are required to strengthen communication and cooperation with hydraulic subregions across administrative regions. However, when managing NOP through the landscape modifications, measures should be taken to reduce the aggregation of nitrogen sources and increase the fragmentation of nitrogen sinks. As for high aggregation developed and agricultural land regions, the types of land used should be enriched to help the sustainable development. | ||
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| 700 | 1 | |a Li, Bing |4 aut | |
| 700 | 1 | |a Wang, Chun |4 aut | |
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10.1007/s11356-021-15842-x doi (DE-627)SPR046928472 (SPR)s11356-021-15842-x-e DE-627 ger DE-627 rakwb eng Wang, Ya’nan verfasserin aut Measuring the zonal responses of nitrogen output to landscape pattern in a flatland with river network: a case study in Taihu Lake Basin, China 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Landscape pattern changes induced by rapid urbanization and intensified agricultural activities have exerted great pressure on regional water purification services. Relationship between landscape metrics and nitrogen-related ecosystem services has been a major concern of many scholars and has been widely used for guidance for land use and cover (LULC) management. However, clear zonal differences may exist, especially in highly developed reticular river network area, thus limiting our understanding of nitrogen output (NOP) to landscape pattern in the details. The spatial distribution of regional NOP was obtained based on the InVEST model. The zonal responses of NOP to landscape patter were examined under hydraulic subregions and subbasin scale. The results show that the unit value of average NOP in the Taihu Lake Basin (TLB) was 146.14 (kg/$ km^{2} $), and the total output reached 23677.92 t in 2020. The simulation NOP showed reasonable agreement with verified water quality observations in the lake inlet stations, with an R2 of 0.76. In terms of space composition, merely cropland have significant effects on NOP in the whole basin scale, while the explanatory variables include cropland and developed land in Pudong (PD), Puxi (PX), Wuchengxiyu (WC), and Hangjiahu (HJ) regions. In Huxi (HX) and Yangchengdianmao (YC) regions, cropland and forest are the significant impact types, while in (Zhexi) ZX region, cropland, developed land, and forest are significant impact types. In the space configuration, the percentage of landscape (PLAND) or largest patch index (LPI) of cropland showed positive effects about NOP, whether in the whole basin or the hydraulic subregions. Edge density (ED) (−3.48), number of patches (NP) (−3.91), and percentage of like adjacencies (PLAND) (−2.80) of the forest exhibit negative correlations with NOP, in the HX, ZX, and YC region, respectively. It displays diversiform in the response of NOP to the landscape metric of developed land, which speculate that the heterogeneity of developed land can also have a constraint on NOP, in the highly urbanized areas with less forest area. In addition, the total nitrogen output of the TLB needs to be controlled, especially in HJ region which was identified as the sensitive area of pollution sources with the largest NOP and should be paid more attention to. Compared with the administrative management unit, it is more reasonable to control and manage the pollution sources by referring to the hydraulic subregions and subbasin units. Senior managers are required to strengthen communication and cooperation with hydraulic subregions across administrative regions. However, when managing NOP through the landscape modifications, measures should be taken to reduce the aggregation of nitrogen sources and increase the fragmentation of nitrogen sinks. As for high aggregation developed and agricultural land regions, the types of land used should be enriched to help the sustainable development. Landscape pattern metrics (dpeaa)DE-He213 Nitrogen output (dpeaa)DE-He213 InVEST model (dpeaa)DE-He213 Stepwise multiple regression analysis (dpeaa)DE-He213 Taihu Lake Basin (dpeaa)DE-He213 Yang, Guishan aut Li, Bing aut Wang, Chun aut Su, Weizhong aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 29(2022), 23 vom: 18. Jan., Seite 34624-34636 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:29 year:2022 number:23 day:18 month:01 pages:34624-34636 https://dx.doi.org/10.1007/s11356-021-15842-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 29 2022 23 18 01 34624-34636 |
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10.1007/s11356-021-15842-x doi (DE-627)SPR046928472 (SPR)s11356-021-15842-x-e DE-627 ger DE-627 rakwb eng Wang, Ya’nan verfasserin aut Measuring the zonal responses of nitrogen output to landscape pattern in a flatland with river network: a case study in Taihu Lake Basin, China 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Landscape pattern changes induced by rapid urbanization and intensified agricultural activities have exerted great pressure on regional water purification services. Relationship between landscape metrics and nitrogen-related ecosystem services has been a major concern of many scholars and has been widely used for guidance for land use and cover (LULC) management. However, clear zonal differences may exist, especially in highly developed reticular river network area, thus limiting our understanding of nitrogen output (NOP) to landscape pattern in the details. The spatial distribution of regional NOP was obtained based on the InVEST model. The zonal responses of NOP to landscape patter were examined under hydraulic subregions and subbasin scale. The results show that the unit value of average NOP in the Taihu Lake Basin (TLB) was 146.14 (kg/$ km^{2} $), and the total output reached 23677.92 t in 2020. The simulation NOP showed reasonable agreement with verified water quality observations in the lake inlet stations, with an R2 of 0.76. In terms of space composition, merely cropland have significant effects on NOP in the whole basin scale, while the explanatory variables include cropland and developed land in Pudong (PD), Puxi (PX), Wuchengxiyu (WC), and Hangjiahu (HJ) regions. In Huxi (HX) and Yangchengdianmao (YC) regions, cropland and forest are the significant impact types, while in (Zhexi) ZX region, cropland, developed land, and forest are significant impact types. In the space configuration, the percentage of landscape (PLAND) or largest patch index (LPI) of cropland showed positive effects about NOP, whether in the whole basin or the hydraulic subregions. Edge density (ED) (−3.48), number of patches (NP) (−3.91), and percentage of like adjacencies (PLAND) (−2.80) of the forest exhibit negative correlations with NOP, in the HX, ZX, and YC region, respectively. It displays diversiform in the response of NOP to the landscape metric of developed land, which speculate that the heterogeneity of developed land can also have a constraint on NOP, in the highly urbanized areas with less forest area. In addition, the total nitrogen output of the TLB needs to be controlled, especially in HJ region which was identified as the sensitive area of pollution sources with the largest NOP and should be paid more attention to. Compared with the administrative management unit, it is more reasonable to control and manage the pollution sources by referring to the hydraulic subregions and subbasin units. Senior managers are required to strengthen communication and cooperation with hydraulic subregions across administrative regions. However, when managing NOP through the landscape modifications, measures should be taken to reduce the aggregation of nitrogen sources and increase the fragmentation of nitrogen sinks. As for high aggregation developed and agricultural land regions, the types of land used should be enriched to help the sustainable development. Landscape pattern metrics (dpeaa)DE-He213 Nitrogen output (dpeaa)DE-He213 InVEST model (dpeaa)DE-He213 Stepwise multiple regression analysis (dpeaa)DE-He213 Taihu Lake Basin (dpeaa)DE-He213 Yang, Guishan aut Li, Bing aut Wang, Chun aut Su, Weizhong aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 29(2022), 23 vom: 18. Jan., Seite 34624-34636 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:29 year:2022 number:23 day:18 month:01 pages:34624-34636 https://dx.doi.org/10.1007/s11356-021-15842-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 29 2022 23 18 01 34624-34636 |
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10.1007/s11356-021-15842-x doi (DE-627)SPR046928472 (SPR)s11356-021-15842-x-e DE-627 ger DE-627 rakwb eng Wang, Ya’nan verfasserin aut Measuring the zonal responses of nitrogen output to landscape pattern in a flatland with river network: a case study in Taihu Lake Basin, China 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Landscape pattern changes induced by rapid urbanization and intensified agricultural activities have exerted great pressure on regional water purification services. Relationship between landscape metrics and nitrogen-related ecosystem services has been a major concern of many scholars and has been widely used for guidance for land use and cover (LULC) management. However, clear zonal differences may exist, especially in highly developed reticular river network area, thus limiting our understanding of nitrogen output (NOP) to landscape pattern in the details. The spatial distribution of regional NOP was obtained based on the InVEST model. The zonal responses of NOP to landscape patter were examined under hydraulic subregions and subbasin scale. The results show that the unit value of average NOP in the Taihu Lake Basin (TLB) was 146.14 (kg/$ km^{2} $), and the total output reached 23677.92 t in 2020. The simulation NOP showed reasonable agreement with verified water quality observations in the lake inlet stations, with an R2 of 0.76. In terms of space composition, merely cropland have significant effects on NOP in the whole basin scale, while the explanatory variables include cropland and developed land in Pudong (PD), Puxi (PX), Wuchengxiyu (WC), and Hangjiahu (HJ) regions. In Huxi (HX) and Yangchengdianmao (YC) regions, cropland and forest are the significant impact types, while in (Zhexi) ZX region, cropland, developed land, and forest are significant impact types. In the space configuration, the percentage of landscape (PLAND) or largest patch index (LPI) of cropland showed positive effects about NOP, whether in the whole basin or the hydraulic subregions. Edge density (ED) (−3.48), number of patches (NP) (−3.91), and percentage of like adjacencies (PLAND) (−2.80) of the forest exhibit negative correlations with NOP, in the HX, ZX, and YC region, respectively. It displays diversiform in the response of NOP to the landscape metric of developed land, which speculate that the heterogeneity of developed land can also have a constraint on NOP, in the highly urbanized areas with less forest area. In addition, the total nitrogen output of the TLB needs to be controlled, especially in HJ region which was identified as the sensitive area of pollution sources with the largest NOP and should be paid more attention to. Compared with the administrative management unit, it is more reasonable to control and manage the pollution sources by referring to the hydraulic subregions and subbasin units. Senior managers are required to strengthen communication and cooperation with hydraulic subregions across administrative regions. However, when managing NOP through the landscape modifications, measures should be taken to reduce the aggregation of nitrogen sources and increase the fragmentation of nitrogen sinks. As for high aggregation developed and agricultural land regions, the types of land used should be enriched to help the sustainable development. Landscape pattern metrics (dpeaa)DE-He213 Nitrogen output (dpeaa)DE-He213 InVEST model (dpeaa)DE-He213 Stepwise multiple regression analysis (dpeaa)DE-He213 Taihu Lake Basin (dpeaa)DE-He213 Yang, Guishan aut Li, Bing aut Wang, Chun aut Su, Weizhong aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 29(2022), 23 vom: 18. Jan., Seite 34624-34636 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:29 year:2022 number:23 day:18 month:01 pages:34624-34636 https://dx.doi.org/10.1007/s11356-021-15842-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 29 2022 23 18 01 34624-34636 |
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10.1007/s11356-021-15842-x doi (DE-627)SPR046928472 (SPR)s11356-021-15842-x-e DE-627 ger DE-627 rakwb eng Wang, Ya’nan verfasserin aut Measuring the zonal responses of nitrogen output to landscape pattern in a flatland with river network: a case study in Taihu Lake Basin, China 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Landscape pattern changes induced by rapid urbanization and intensified agricultural activities have exerted great pressure on regional water purification services. Relationship between landscape metrics and nitrogen-related ecosystem services has been a major concern of many scholars and has been widely used for guidance for land use and cover (LULC) management. However, clear zonal differences may exist, especially in highly developed reticular river network area, thus limiting our understanding of nitrogen output (NOP) to landscape pattern in the details. The spatial distribution of regional NOP was obtained based on the InVEST model. The zonal responses of NOP to landscape patter were examined under hydraulic subregions and subbasin scale. The results show that the unit value of average NOP in the Taihu Lake Basin (TLB) was 146.14 (kg/$ km^{2} $), and the total output reached 23677.92 t in 2020. The simulation NOP showed reasonable agreement with verified water quality observations in the lake inlet stations, with an R2 of 0.76. In terms of space composition, merely cropland have significant effects on NOP in the whole basin scale, while the explanatory variables include cropland and developed land in Pudong (PD), Puxi (PX), Wuchengxiyu (WC), and Hangjiahu (HJ) regions. In Huxi (HX) and Yangchengdianmao (YC) regions, cropland and forest are the significant impact types, while in (Zhexi) ZX region, cropland, developed land, and forest are significant impact types. In the space configuration, the percentage of landscape (PLAND) or largest patch index (LPI) of cropland showed positive effects about NOP, whether in the whole basin or the hydraulic subregions. Edge density (ED) (−3.48), number of patches (NP) (−3.91), and percentage of like adjacencies (PLAND) (−2.80) of the forest exhibit negative correlations with NOP, in the HX, ZX, and YC region, respectively. It displays diversiform in the response of NOP to the landscape metric of developed land, which speculate that the heterogeneity of developed land can also have a constraint on NOP, in the highly urbanized areas with less forest area. In addition, the total nitrogen output of the TLB needs to be controlled, especially in HJ region which was identified as the sensitive area of pollution sources with the largest NOP and should be paid more attention to. Compared with the administrative management unit, it is more reasonable to control and manage the pollution sources by referring to the hydraulic subregions and subbasin units. Senior managers are required to strengthen communication and cooperation with hydraulic subregions across administrative regions. However, when managing NOP through the landscape modifications, measures should be taken to reduce the aggregation of nitrogen sources and increase the fragmentation of nitrogen sinks. As for high aggregation developed and agricultural land regions, the types of land used should be enriched to help the sustainable development. Landscape pattern metrics (dpeaa)DE-He213 Nitrogen output (dpeaa)DE-He213 InVEST model (dpeaa)DE-He213 Stepwise multiple regression analysis (dpeaa)DE-He213 Taihu Lake Basin (dpeaa)DE-He213 Yang, Guishan aut Li, Bing aut Wang, Chun aut Su, Weizhong aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 29(2022), 23 vom: 18. Jan., Seite 34624-34636 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:29 year:2022 number:23 day:18 month:01 pages:34624-34636 https://dx.doi.org/10.1007/s11356-021-15842-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 29 2022 23 18 01 34624-34636 |
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10.1007/s11356-021-15842-x doi (DE-627)SPR046928472 (SPR)s11356-021-15842-x-e DE-627 ger DE-627 rakwb eng Wang, Ya’nan verfasserin aut Measuring the zonal responses of nitrogen output to landscape pattern in a flatland with river network: a case study in Taihu Lake Basin, China 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Landscape pattern changes induced by rapid urbanization and intensified agricultural activities have exerted great pressure on regional water purification services. Relationship between landscape metrics and nitrogen-related ecosystem services has been a major concern of many scholars and has been widely used for guidance for land use and cover (LULC) management. However, clear zonal differences may exist, especially in highly developed reticular river network area, thus limiting our understanding of nitrogen output (NOP) to landscape pattern in the details. The spatial distribution of regional NOP was obtained based on the InVEST model. The zonal responses of NOP to landscape patter were examined under hydraulic subregions and subbasin scale. The results show that the unit value of average NOP in the Taihu Lake Basin (TLB) was 146.14 (kg/$ km^{2} $), and the total output reached 23677.92 t in 2020. The simulation NOP showed reasonable agreement with verified water quality observations in the lake inlet stations, with an R2 of 0.76. In terms of space composition, merely cropland have significant effects on NOP in the whole basin scale, while the explanatory variables include cropland and developed land in Pudong (PD), Puxi (PX), Wuchengxiyu (WC), and Hangjiahu (HJ) regions. In Huxi (HX) and Yangchengdianmao (YC) regions, cropland and forest are the significant impact types, while in (Zhexi) ZX region, cropland, developed land, and forest are significant impact types. In the space configuration, the percentage of landscape (PLAND) or largest patch index (LPI) of cropland showed positive effects about NOP, whether in the whole basin or the hydraulic subregions. Edge density (ED) (−3.48), number of patches (NP) (−3.91), and percentage of like adjacencies (PLAND) (−2.80) of the forest exhibit negative correlations with NOP, in the HX, ZX, and YC region, respectively. It displays diversiform in the response of NOP to the landscape metric of developed land, which speculate that the heterogeneity of developed land can also have a constraint on NOP, in the highly urbanized areas with less forest area. In addition, the total nitrogen output of the TLB needs to be controlled, especially in HJ region which was identified as the sensitive area of pollution sources with the largest NOP and should be paid more attention to. Compared with the administrative management unit, it is more reasonable to control and manage the pollution sources by referring to the hydraulic subregions and subbasin units. Senior managers are required to strengthen communication and cooperation with hydraulic subregions across administrative regions. However, when managing NOP through the landscape modifications, measures should be taken to reduce the aggregation of nitrogen sources and increase the fragmentation of nitrogen sinks. As for high aggregation developed and agricultural land regions, the types of land used should be enriched to help the sustainable development. Landscape pattern metrics (dpeaa)DE-He213 Nitrogen output (dpeaa)DE-He213 InVEST model (dpeaa)DE-He213 Stepwise multiple regression analysis (dpeaa)DE-He213 Taihu Lake Basin (dpeaa)DE-He213 Yang, Guishan aut Li, Bing aut Wang, Chun aut Su, Weizhong aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 29(2022), 23 vom: 18. Jan., Seite 34624-34636 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:29 year:2022 number:23 day:18 month:01 pages:34624-34636 https://dx.doi.org/10.1007/s11356-021-15842-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 29 2022 23 18 01 34624-34636 |
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Enthalten in Environmental science and pollution research 29(2022), 23 vom: 18. Jan., Seite 34624-34636 volume:29 year:2022 number:23 day:18 month:01 pages:34624-34636 |
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Wang, Ya’nan @@aut@@ Yang, Guishan @@aut@@ Li, Bing @@aut@@ Wang, Chun @@aut@@ Su, Weizhong @@aut@@ |
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Relationship between landscape metrics and nitrogen-related ecosystem services has been a major concern of many scholars and has been widely used for guidance for land use and cover (LULC) management. However, clear zonal differences may exist, especially in highly developed reticular river network area, thus limiting our understanding of nitrogen output (NOP) to landscape pattern in the details. The spatial distribution of regional NOP was obtained based on the InVEST model. The zonal responses of NOP to landscape patter were examined under hydraulic subregions and subbasin scale. The results show that the unit value of average NOP in the Taihu Lake Basin (TLB) was 146.14 (kg/$ km^{2} $), and the total output reached 23677.92 t in 2020. The simulation NOP showed reasonable agreement with verified water quality observations in the lake inlet stations, with an R2 of 0.76. In terms of space composition, merely cropland have significant effects on NOP in the whole basin scale, while the explanatory variables include cropland and developed land in Pudong (PD), Puxi (PX), Wuchengxiyu (WC), and Hangjiahu (HJ) regions. In Huxi (HX) and Yangchengdianmao (YC) regions, cropland and forest are the significant impact types, while in (Zhexi) ZX region, cropland, developed land, and forest are significant impact types. In the space configuration, the percentage of landscape (PLAND) or largest patch index (LPI) of cropland showed positive effects about NOP, whether in the whole basin or the hydraulic subregions. Edge density (ED) (−3.48), number of patches (NP) (−3.91), and percentage of like adjacencies (PLAND) (−2.80) of the forest exhibit negative correlations with NOP, in the HX, ZX, and YC region, respectively. It displays diversiform in the response of NOP to the landscape metric of developed land, which speculate that the heterogeneity of developed land can also have a constraint on NOP, in the highly urbanized areas with less forest area. In addition, the total nitrogen output of the TLB needs to be controlled, especially in HJ region which was identified as the sensitive area of pollution sources with the largest NOP and should be paid more attention to. Compared with the administrative management unit, it is more reasonable to control and manage the pollution sources by referring to the hydraulic subregions and subbasin units. Senior managers are required to strengthen communication and cooperation with hydraulic subregions across administrative regions. However, when managing NOP through the landscape modifications, measures should be taken to reduce the aggregation of nitrogen sources and increase the fragmentation of nitrogen sinks. As for high aggregation developed and agricultural land regions, the types of land used should be enriched to help the sustainable development.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Landscape pattern metrics</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Nitrogen output</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">InVEST model</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Stepwise multiple regression analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Taihu Lake Basin</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Guishan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Bing</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Chun</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Su, Weizhong</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Environmental science and pollution research</subfield><subfield code="d">Berlin : Springer, 1994</subfield><subfield code="g">29(2022), 23 vom: 18. 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|
| author |
Wang, Ya’nan |
| spellingShingle |
Wang, Ya’nan misc Landscape pattern metrics misc Nitrogen output misc InVEST model misc Stepwise multiple regression analysis misc Taihu Lake Basin Measuring the zonal responses of nitrogen output to landscape pattern in a flatland with river network: a case study in Taihu Lake Basin, China |
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Measuring the zonal responses of nitrogen output to landscape pattern in a flatland with river network: a case study in Taihu Lake Basin, China Landscape pattern metrics (dpeaa)DE-He213 Nitrogen output (dpeaa)DE-He213 InVEST model (dpeaa)DE-He213 Stepwise multiple regression analysis (dpeaa)DE-He213 Taihu Lake Basin (dpeaa)DE-He213 |
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misc Landscape pattern metrics misc Nitrogen output misc InVEST model misc Stepwise multiple regression analysis misc Taihu Lake Basin |
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misc Landscape pattern metrics misc Nitrogen output misc InVEST model misc Stepwise multiple regression analysis misc Taihu Lake Basin |
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Measuring the zonal responses of nitrogen output to landscape pattern in a flatland with river network: a case study in Taihu Lake Basin, China |
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Measuring the zonal responses of nitrogen output to landscape pattern in a flatland with river network: a case study in Taihu Lake Basin, China |
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Wang, Ya’nan |
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Wang, Ya’nan Yang, Guishan Li, Bing Wang, Chun Su, Weizhong |
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10.1007/s11356-021-15842-x |
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measuring the zonal responses of nitrogen output to landscape pattern in a flatland with river network: a case study in taihu lake basin, china |
| title_auth |
Measuring the zonal responses of nitrogen output to landscape pattern in a flatland with river network: a case study in Taihu Lake Basin, China |
| abstract |
Abstract Landscape pattern changes induced by rapid urbanization and intensified agricultural activities have exerted great pressure on regional water purification services. Relationship between landscape metrics and nitrogen-related ecosystem services has been a major concern of many scholars and has been widely used for guidance for land use and cover (LULC) management. However, clear zonal differences may exist, especially in highly developed reticular river network area, thus limiting our understanding of nitrogen output (NOP) to landscape pattern in the details. The spatial distribution of regional NOP was obtained based on the InVEST model. The zonal responses of NOP to landscape patter were examined under hydraulic subregions and subbasin scale. The results show that the unit value of average NOP in the Taihu Lake Basin (TLB) was 146.14 (kg/$ km^{2} $), and the total output reached 23677.92 t in 2020. The simulation NOP showed reasonable agreement with verified water quality observations in the lake inlet stations, with an R2 of 0.76. In terms of space composition, merely cropland have significant effects on NOP in the whole basin scale, while the explanatory variables include cropland and developed land in Pudong (PD), Puxi (PX), Wuchengxiyu (WC), and Hangjiahu (HJ) regions. In Huxi (HX) and Yangchengdianmao (YC) regions, cropland and forest are the significant impact types, while in (Zhexi) ZX region, cropland, developed land, and forest are significant impact types. In the space configuration, the percentage of landscape (PLAND) or largest patch index (LPI) of cropland showed positive effects about NOP, whether in the whole basin or the hydraulic subregions. Edge density (ED) (−3.48), number of patches (NP) (−3.91), and percentage of like adjacencies (PLAND) (−2.80) of the forest exhibit negative correlations with NOP, in the HX, ZX, and YC region, respectively. It displays diversiform in the response of NOP to the landscape metric of developed land, which speculate that the heterogeneity of developed land can also have a constraint on NOP, in the highly urbanized areas with less forest area. In addition, the total nitrogen output of the TLB needs to be controlled, especially in HJ region which was identified as the sensitive area of pollution sources with the largest NOP and should be paid more attention to. Compared with the administrative management unit, it is more reasonable to control and manage the pollution sources by referring to the hydraulic subregions and subbasin units. Senior managers are required to strengthen communication and cooperation with hydraulic subregions across administrative regions. However, when managing NOP through the landscape modifications, measures should be taken to reduce the aggregation of nitrogen sources and increase the fragmentation of nitrogen sinks. As for high aggregation developed and agricultural land regions, the types of land used should be enriched to help the sustainable development. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
| abstractGer |
Abstract Landscape pattern changes induced by rapid urbanization and intensified agricultural activities have exerted great pressure on regional water purification services. Relationship between landscape metrics and nitrogen-related ecosystem services has been a major concern of many scholars and has been widely used for guidance for land use and cover (LULC) management. However, clear zonal differences may exist, especially in highly developed reticular river network area, thus limiting our understanding of nitrogen output (NOP) to landscape pattern in the details. The spatial distribution of regional NOP was obtained based on the InVEST model. The zonal responses of NOP to landscape patter were examined under hydraulic subregions and subbasin scale. The results show that the unit value of average NOP in the Taihu Lake Basin (TLB) was 146.14 (kg/$ km^{2} $), and the total output reached 23677.92 t in 2020. The simulation NOP showed reasonable agreement with verified water quality observations in the lake inlet stations, with an R2 of 0.76. In terms of space composition, merely cropland have significant effects on NOP in the whole basin scale, while the explanatory variables include cropland and developed land in Pudong (PD), Puxi (PX), Wuchengxiyu (WC), and Hangjiahu (HJ) regions. In Huxi (HX) and Yangchengdianmao (YC) regions, cropland and forest are the significant impact types, while in (Zhexi) ZX region, cropland, developed land, and forest are significant impact types. In the space configuration, the percentage of landscape (PLAND) or largest patch index (LPI) of cropland showed positive effects about NOP, whether in the whole basin or the hydraulic subregions. Edge density (ED) (−3.48), number of patches (NP) (−3.91), and percentage of like adjacencies (PLAND) (−2.80) of the forest exhibit negative correlations with NOP, in the HX, ZX, and YC region, respectively. It displays diversiform in the response of NOP to the landscape metric of developed land, which speculate that the heterogeneity of developed land can also have a constraint on NOP, in the highly urbanized areas with less forest area. In addition, the total nitrogen output of the TLB needs to be controlled, especially in HJ region which was identified as the sensitive area of pollution sources with the largest NOP and should be paid more attention to. Compared with the administrative management unit, it is more reasonable to control and manage the pollution sources by referring to the hydraulic subregions and subbasin units. Senior managers are required to strengthen communication and cooperation with hydraulic subregions across administrative regions. However, when managing NOP through the landscape modifications, measures should be taken to reduce the aggregation of nitrogen sources and increase the fragmentation of nitrogen sinks. As for high aggregation developed and agricultural land regions, the types of land used should be enriched to help the sustainable development. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract Landscape pattern changes induced by rapid urbanization and intensified agricultural activities have exerted great pressure on regional water purification services. Relationship between landscape metrics and nitrogen-related ecosystem services has been a major concern of many scholars and has been widely used for guidance for land use and cover (LULC) management. However, clear zonal differences may exist, especially in highly developed reticular river network area, thus limiting our understanding of nitrogen output (NOP) to landscape pattern in the details. The spatial distribution of regional NOP was obtained based on the InVEST model. The zonal responses of NOP to landscape patter were examined under hydraulic subregions and subbasin scale. The results show that the unit value of average NOP in the Taihu Lake Basin (TLB) was 146.14 (kg/$ km^{2} $), and the total output reached 23677.92 t in 2020. The simulation NOP showed reasonable agreement with verified water quality observations in the lake inlet stations, with an R2 of 0.76. In terms of space composition, merely cropland have significant effects on NOP in the whole basin scale, while the explanatory variables include cropland and developed land in Pudong (PD), Puxi (PX), Wuchengxiyu (WC), and Hangjiahu (HJ) regions. In Huxi (HX) and Yangchengdianmao (YC) regions, cropland and forest are the significant impact types, while in (Zhexi) ZX region, cropland, developed land, and forest are significant impact types. In the space configuration, the percentage of landscape (PLAND) or largest patch index (LPI) of cropland showed positive effects about NOP, whether in the whole basin or the hydraulic subregions. Edge density (ED) (−3.48), number of patches (NP) (−3.91), and percentage of like adjacencies (PLAND) (−2.80) of the forest exhibit negative correlations with NOP, in the HX, ZX, and YC region, respectively. It displays diversiform in the response of NOP to the landscape metric of developed land, which speculate that the heterogeneity of developed land can also have a constraint on NOP, in the highly urbanized areas with less forest area. In addition, the total nitrogen output of the TLB needs to be controlled, especially in HJ region which was identified as the sensitive area of pollution sources with the largest NOP and should be paid more attention to. Compared with the administrative management unit, it is more reasonable to control and manage the pollution sources by referring to the hydraulic subregions and subbasin units. Senior managers are required to strengthen communication and cooperation with hydraulic subregions across administrative regions. However, when managing NOP through the landscape modifications, measures should be taken to reduce the aggregation of nitrogen sources and increase the fragmentation of nitrogen sinks. As for high aggregation developed and agricultural land regions, the types of land used should be enriched to help the sustainable development. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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Measuring the zonal responses of nitrogen output to landscape pattern in a flatland with river network: a case study in Taihu Lake Basin, China |
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Yang, Guishan Li, Bing Wang, Chun Su, Weizhong |
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| score |
7.4005365 |