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Modelling the impact of future socio-economic and climate change scenarios on river microbial water quality.

International journal of hygiene and environmental health

Islam MMM, Iqbal MS, Leemans R, Hofstra N.
PMID: 29217424
Int J Hyg Environ Health. 2018 Mar;221(2):283-292. doi: 10.1016/j.ijheh.2017.11.006. Epub 2017 Dec 05.

Microbial surface water quality is important, as it is related to health risk when the population is exposed through drinking, recreation or consumption of irrigated vegetables. The microbial surface water quality is expected to change with socio-economic development and...

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Islam MMM, Iqbal MS, Leemans R, et al. Modelling the impact of future socio-economic and climate change scenarios on river microbial water quality. Int J Hyg Environ Health. 2017;221(2):283-292doi: 10.1016/j.ijheh.2017.11.006.
Islam, M. M. M., Iqbal, M. S., Leemans, R., & Hofstra, N. (2018). Modelling the impact of future socio-economic and climate change scenarios on river microbial water quality. International journal of hygiene and environmental health, 221(2), 283-292. https://doi.org/10.1016/j.ijheh.2017.11.006
Islam, M M Majedul, et al. "Modelling the impact of future socio-economic and climate change scenarios on river microbial water quality." International journal of hygiene and environmental health vol. 221,2 (2018): 283-292. doi: https://doi.org/10.1016/j.ijheh.2017.11.006
Islam MMM, Iqbal MS, Leemans R, Hofstra N. Modelling the impact of future socio-economic and climate change scenarios on river microbial water quality. Int J Hyg Environ Health. 2018 Mar;221(2):283-292. doi: 10.1016/j.ijheh.2017.11.006. Epub 2017 Dec 05. PMID: 29217424.
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Estimation of water consumption for ecosystems based on Vegetation Interfaces Processes Model: A case study of the Aksu River Basin, Northwest China.

The Science of the total environment

Yang P, Xia J, Zhan C, Mo X, Chen X, Hu S, Chen J.
PMID: 28915455
Sci Total Environ. 2018 Feb 01;613:186-195. doi: 10.1016/j.scitotenv.2017.09.045. Epub 2017 Sep 12.

Based on the Moderate Resolution Imaging Spectroradiometer (MODIS) - Normalized Difference Vegetation Index (NDVI), the Vegetation Interfaces Processes (VIP) model simulated the spatio-temporal patterns of actual evapotranspiration (ET) and the water consumption of different ecosystems in the Aksu River...

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Yang P, Xia J, Zhan C, et al. Estimation of water consumption for ecosystems based on Vegetation Interfaces Processes Model: A case study of the Aksu River Basin, Northwest China. Sci Total Environ. 2017;613:186-195doi: 10.1016/j.scitotenv.2017.09.045.
Yang, P., Xia, J., Zhan, C., Mo, X., Chen, X., Hu, S., & Chen, J. (2018). Estimation of water consumption for ecosystems based on Vegetation Interfaces Processes Model: A case study of the Aksu River Basin, Northwest China. The Science of the total environment, 613186-195. https://doi.org/10.1016/j.scitotenv.2017.09.045
Yang, Peng, et al. "Estimation of water consumption for ecosystems based on Vegetation Interfaces Processes Model: A case study of the Aksu River Basin, Northwest China." The Science of the total environment vol. 613 (2018): 186-195. doi: https://doi.org/10.1016/j.scitotenv.2017.09.045
Yang P, Xia J, Zhan C, Mo X, Chen X, Hu S, Chen J. Estimation of water consumption for ecosystems based on Vegetation Interfaces Processes Model: A case study of the Aksu River Basin, Northwest China. Sci Total Environ. 2018 Feb 01;613:186-195. doi: 10.1016/j.scitotenv.2017.09.045. Epub 2017 Sep 12. PMID: 28915455.
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Integrated modelling and monitoring at different river basin scales under global change.

The Science of the total environment

Elorza FJ, Navarro-Ortega A, Barceló D.
PMID: 23031291
Sci Total Environ. 2012 Dec 01;440:1-2. doi: 10.1016/j.scitotenv.2012.08.051. Epub 2012 Sep 29.

No abstract available.

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Elorza FJ, Navarro-Ortega A, Barceló D. Integrated modelling and monitoring at different river basin scales under global change. Sci Total Environ. 2012;440:1-2doi: 10.1016/j.scitotenv.2012.08.051.
Elorza, F. J., Navarro-Ortega, A., & Barceló, D. (2012). Integrated modelling and monitoring at different river basin scales under global change. The Science of the total environment, 4401-2. https://doi.org/10.1016/j.scitotenv.2012.08.051
Elorza, Fco Javier, et al. "Integrated modelling and monitoring at different river basin scales under global change." The Science of the total environment vol. 440 (2012): 1-2. doi: https://doi.org/10.1016/j.scitotenv.2012.08.051
Elorza FJ, Navarro-Ortega A, Barceló D. Integrated modelling and monitoring at different river basin scales under global change. Sci Total Environ. 2012 Dec 01;440:1-2. doi: 10.1016/j.scitotenv.2012.08.051. Epub 2012 Sep 29. PMID: 23031291.
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Responses of meteorological drought-hydrological drought propagation to watershed scales in the upper Huaihe River basin, China.

Environmental science and pollution research international

Yu M, Liu X, Li Q.
PMID: 31502051
Environ Sci Pollut Res Int. 2020 May;27(15):17561-17570. doi: 10.1007/s11356-019-06413-2. Epub 2019 Sep 09.

Spatial scale is a crucial factor in drought propagation and characterization. To investigate the response behavior of the meteorological drought-hydrological drought propagation to different watershed scales, three nested subbasins in the upper Huaihe River basin were selected as case...

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Yu M, Liu X, Li Q. Responses of meteorological drought-hydrological drought propagation to watershed scales in the upper Huaihe River basin, China. Environ Sci Pollut Res Int. 2019;27(15):17561-17570doi: 10.1007/s11356-019-06413-2.
Yu, M., Liu, X., & Li, Q. (2020). Responses of meteorological drought-hydrological drought propagation to watershed scales in the upper Huaihe River basin, China. Environmental science and pollution research international, 27(15), 17561-17570. https://doi.org/10.1007/s11356-019-06413-2
Yu, Meixiu, et al. "Responses of meteorological drought-hydrological drought propagation to watershed scales in the upper Huaihe River basin, China." Environmental science and pollution research international vol. 27,15 (2020): 17561-17570. doi: https://doi.org/10.1007/s11356-019-06413-2
Yu M, Liu X, Li Q. Responses of meteorological drought-hydrological drought propagation to watershed scales in the upper Huaihe River basin, China. Environ Sci Pollut Res Int. 2020 May;27(15):17561-17570. doi: 10.1007/s11356-019-06413-2. Epub 2019 Sep 09. PMID: 31502051.
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[A process of aquatic ecological function regionalization: The dual tree framework and conceptual model].

Ying yong sheng tai xue bao = The journal of applied ecology

Guo SH, Wu B.
PMID: 29696902
Ying Yong Sheng Tai Xue Bao. 2017 Dec;28(12):4051-4056. doi: 10.13287/j.1001-9332.201712.015.

Aquatic ecological regionalization and aquatic ecological function regionalization are the basis of water environmental management of a river basin and rational utilization of an aquatic ecosystem, and have been studied in China for more than ten years. Regarding the...

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Guo SH, Wu B. 水生态功能区划流程:双关系树框架与概念模型. [A process of aquatic ecological function regionalization: The dual tree framework and conceptual model]. Ying Yong Sheng Tai Xue Bao. 2017;28(12):4051-4056doi: 10.13287/j.1001-9332.201712.015.
Guo, S. H., & Wu, B. (2017). 水生态功能区划流程:双关系树框架与概念模型. [A process of aquatic ecological function regionalization: The dual tree framework and conceptual model]. Ying yong sheng tai xue bao = The journal of applied ecology, 28(12), 4051-4056. https://doi.org/10.13287/j.1001-9332.201712.015
Guo, Shu Hai, and Wu, Bo. "水生态功能区划流程:双关系树框架与概念模型." [A process of aquatic ecological function regionalization: The dual tree framework and conceptual model]. Ying yong sheng tai xue bao = The journal of applied ecology vol. 28,12 (2017): 4051-4056. doi: https://doi.org/10.13287/j.1001-9332.201712.015
Guo SH, Wu B. 水生态功能区划流程:双关系树框架与概念模型. [A process of aquatic ecological function regionalization: The dual tree framework and conceptual model]. Ying Yong Sheng Tai Xue Bao. 2017 Dec;28(12):4051-4056. doi: 10.13287/j.1001-9332.201712.015. Chinese. PMID: 29696902.
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Uncertainty analysis in a large-scale water quality integrated catchment modelling study.

Water research

Moreno-Rodenas AM, Tscheikner-Gratl F, Langeveld JG, Clemens FHLR.
PMID: 31015142
Water Res. 2019 Jul 01;158:46-60. doi: 10.1016/j.watres.2019.04.016. Epub 2019 Apr 12.

Receiving water quality simulation in highly urbanised areas requires the integration of several processes occurring at different space-time scales. These integrated catchment models deliver results with a significant uncertainty level associated. Still, uncertainty analysis is seldom applied in practice...

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Moreno-Rodenas AM, Tscheikner-Gratl F, Langeveld JG, et al. Uncertainty analysis in a large-scale water quality integrated catchment modelling study. Water Res. 2019;158:46-60doi: 10.1016/j.watres.2019.04.016.
Moreno-Rodenas, A. M., Tscheikner-Gratl, F., Langeveld, J. G., & Clemens, F. H. L. R. (2019). Uncertainty analysis in a large-scale water quality integrated catchment modelling study. Water research, 15846-60. https://doi.org/10.1016/j.watres.2019.04.016
Moreno-Rodenas, Antonio M, et al. "Uncertainty analysis in a large-scale water quality integrated catchment modelling study." Water research vol. 158 (2019): 46-60. doi: https://doi.org/10.1016/j.watres.2019.04.016
Moreno-Rodenas AM, Tscheikner-Gratl F, Langeveld JG, Clemens FHLR. Uncertainty analysis in a large-scale water quality integrated catchment modelling study. Water Res. 2019 Jul 01;158:46-60. doi: 10.1016/j.watres.2019.04.016. Epub 2019 Apr 12. PMID: 31015142.
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Prioritizing river basins for intensive monitoring and assessment by the US Geological Survey.

Environmental monitoring and assessment

Van Metre PC, Qi S, Deacon J, Dieter C, Driscoll JM, Fienen M, Kenney T, Lambert P, Lesmes D, Mason CA, Mueller-Solger A, Musgrove M, Painter J, Rosenberry D, Sprague L, Tesoriero AJ, Windham-Myers L, Wolock D.
PMID: 32594332
Environ Monit Assess. 2020 Jun 27;192(7):458. doi: 10.1007/s10661-020-08403-1.

The US Geological Survey (USGS) is currently (2020) integrating its water science programs to better address the nation's greatest water resource challenges now and into the future. This integration will rely, in part, on data from 10 or more...

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Van Metre PC, Qi S, Deacon J, et al. Prioritizing river basins for intensive monitoring and assessment by the US Geological Survey. Environ Monit Assess. 2020;192(7):458doi: 10.1007/s10661-020-08403-1.
Van Metre, P. C., Qi, S., Deacon, J., Dieter, C., Driscoll, J. M., Fienen, M., Kenney, T., Lambert, P., Lesmes, D., Mason, C. A., Mueller-Solger, A., Musgrove, M., Painter, J., Rosenberry, D., Sprague, L., Tesoriero, A. J., Windham-Myers, L., & Wolock, D. (2020). Prioritizing river basins for intensive monitoring and assessment by the US Geological Survey. Environmental monitoring and assessment, 192(7), 458. https://doi.org/10.1007/s10661-020-08403-1
Van Metre, Peter C, et al. "Prioritizing river basins for intensive monitoring and assessment by the US Geological Survey." Environmental monitoring and assessment vol. 192,7 (2020): 458. doi: https://doi.org/10.1007/s10661-020-08403-1
Van Metre PC, Qi S, Deacon J, Dieter C, Driscoll JM, Fienen M, Kenney T, Lambert P, Lesmes D, Mason CA, Mueller-Solger A, Musgrove M, Painter J, Rosenberry D, Sprague L, Tesoriero AJ, Windham-Myers L, Wolock D. Prioritizing river basins for intensive monitoring and assessment by the US Geological Survey. Environ Monit Assess. 2020 Jun 27;192(7):458. doi: 10.1007/s10661-020-08403-1. PMID: 32594332; PMCID: PMC7320945.
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[Extracting method of tidal creek features under heterogeneous background at Yellow River Delta using remotely sensed imagery.].

Ying yong sheng tai xue bao = The journal of applied ecology

Wang QW, Gong ZN, Guan HL, Zhang L, Jing R, Wang X.
PMID: 31529885
Ying Yong Sheng Tai Xue Bao. 2019 Sep;30(9):3097-3107. doi: 10.13287/j.1001-9332.201909.019.

The Yellow River Delta exhibits irregular tidal flat, with tidal creeks that vary in width and experience tidal creek current anisotropy. Given such characteristics, the GF-2 multi-spectral image was selected as the data source to characterize the details of...

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Wang QW, Gong ZN, Guan HL, et al. 异质背景下黄河三角洲潮沟的遥感提取方法. [Extracting method of tidal creek features under heterogeneous background at Yellow River Delta using remotely sensed imagery.]. Ying Yong Sheng Tai Xue Bao. 2019;30(9):3097-3107doi: 10.13287/j.1001-9332.201909.019.
Wang, Q. W., Gong, Z. N., Guan, H. L., Zhang, L., Jing, R., & Wang, X. (2019). 异质背景下黄河三角洲潮沟的遥感提取方法. [Extracting method of tidal creek features under heterogeneous background at Yellow River Delta using remotely sensed imagery.]. Ying yong sheng tai xue bao = The journal of applied ecology, 30(9), 3097-3107. https://doi.org/10.13287/j.1001-9332.201909.019
Wang, Qi Wei, et al. "异质背景下黄河三角洲潮沟的遥感提取方法." [Extracting method of tidal creek features under heterogeneous background at Yellow River Delta using remotely sensed imagery.]. Ying yong sheng tai xue bao = The journal of applied ecology vol. 30,9 (2019): 3097-3107. doi: https://doi.org/10.13287/j.1001-9332.201909.019
Wang QW, Gong ZN, Guan HL, Zhang L, Jing R, Wang X. 异质背景下黄河三角洲潮沟的遥感提取方法. [Extracting method of tidal creek features under heterogeneous background at Yellow River Delta using remotely sensed imagery.]. Ying Yong Sheng Tai Xue Bao. 2019 Sep;30(9):3097-3107. doi: 10.13287/j.1001-9332.201909.019. Chinese. PMID: 31529885.
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Dynamic change of vegetation and its response to climate and topographic factors in the Xijiang River basin, China.

Environmental science and pollution research international

Jia L, Li ZB, Xu GC, Ren ZP, Li P, Cheng YT, Zhang YX, Wang B, Zhang JX, Yu S.
PMID: 31970642
Environ Sci Pollut Res Int. 2020 Apr;27(11):11637-11648. doi: 10.1007/s11356-020-07692-w. Epub 2020 Jan 22.

Vegetation plays an important role in the energy exchange, water cycle, carbon cycle, biogeochemical cycle, and maintenance of surface ecosystems. In recent years, regional vegetation cover has changed significantly. This study used statistical analyses, including the Mann-Kendall trend test,...

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Jia L, Li ZB, Xu GC, et al. Dynamic change of vegetation and its response to climate and topographic factors in the Xijiang River basin, China. Environ Sci Pollut Res Int. 2020;27(11):11637-11648doi: 10.1007/s11356-020-07692-w.
Jia, L., Li, Z. B., Xu, G. C., Ren, Z. P., Li, P., Cheng, Y. T., Zhang, Y. X., Wang, B., Zhang, J. X., & Yu, S. (2020). Dynamic change of vegetation and its response to climate and topographic factors in the Xijiang River basin, China. Environmental science and pollution research international, 27(11), 11637-11648. https://doi.org/10.1007/s11356-020-07692-w
Jia, Lu, et al. "Dynamic change of vegetation and its response to climate and topographic factors in the Xijiang River basin, China." Environmental science and pollution research international vol. 27,11 (2020): 11637-11648. doi: https://doi.org/10.1007/s11356-020-07692-w
Jia L, Li ZB, Xu GC, Ren ZP, Li P, Cheng YT, Zhang YX, Wang B, Zhang JX, Yu S. Dynamic change of vegetation and its response to climate and topographic factors in the Xijiang River basin, China. Environ Sci Pollut Res Int. 2020 Apr;27(11):11637-11648. doi: 10.1007/s11356-020-07692-w. Epub 2020 Jan 22. PMID: 31970642.
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Comparing spatial regression to random forests for large environmental data sets.

PloS one

Fox EW, Ver Hoef JM, Olsen AR.
PMID: 32203555
PLoS One. 2020 Mar 23;15(3):e0229509. doi: 10.1371/journal.pone.0229509. eCollection 2020.

Environmental data may be "large" due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates with nonlinear relationships, whereas spatial regression, when using reduced rank methods,...

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Fox EW, Ver Hoef JM, Olsen AR. Comparing spatial regression to random forests for large environmental data sets. PLoS One. 2020;15(3):e0229509doi: 10.1371/journal.pone.0229509.
Fox, E. W., Ver Hoef, J. M., & Olsen, A. R. (2020). Comparing spatial regression to random forests for large environmental data sets. PloS one, 15(3), e0229509. https://doi.org/10.1371/journal.pone.0229509
Fox, Eric W, et al. "Comparing spatial regression to random forests for large environmental data sets." PloS one vol. 15,3 (2020): e0229509. doi: https://doi.org/10.1371/journal.pone.0229509
Fox EW, Ver Hoef JM, Olsen AR. Comparing spatial regression to random forests for large environmental data sets. PLoS One. 2020 Mar 23;15(3):e0229509. doi: 10.1371/journal.pone.0229509. eCollection 2020. PMID: 32203555; PMCID: PMC7089425.
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Characterizing annual flood patterns variation using information and complexity indices.

The Science of the total environment

Al Sawaf MB, Kawanisi K, Xiao C.
PMID: 34740655
Sci Total Environ. 2022 Feb 01;806:151382. doi: 10.1016/j.scitotenv.2021.151382. Epub 2021 Nov 02.

In recent decades, the frequency of torrential rain coupled with various complex flood patterns increased. Since floods are changeable and unpredictable, deeper understanding of flood incidents is necessary for better watershed management. The primary objective of this research is...

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Al Sawaf MB, Kawanisi K, Xiao C. Characterizing annual flood patterns variation using information and complexity indices. Sci Total Environ. 2021;806:151382doi: 10.1016/j.scitotenv.2021.151382.
Al Sawaf, M. B., Kawanisi, K., & Xiao, C. (2022). Characterizing annual flood patterns variation using information and complexity indices. The Science of the total environment, 806151382. https://doi.org/10.1016/j.scitotenv.2021.151382
Al Sawaf, Mohamad Basel, et al. "Characterizing annual flood patterns variation using information and complexity indices." The Science of the total environment vol. 806 (2022): 151382. doi: https://doi.org/10.1016/j.scitotenv.2021.151382
Al Sawaf MB, Kawanisi K, Xiao C. Characterizing annual flood patterns variation using information and complexity indices. Sci Total Environ. 2022 Feb 01;806:151382. doi: 10.1016/j.scitotenv.2021.151382. Epub 2021 Nov 02. PMID: 34740655.
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Improving biological condition assessment accuracy by multimetric index approach with microalgae in streams and lakes.

The Science of the total environment

Liu B, Li D, Chen S, Wu N, Guan Y.
PMID: 33736158
Sci Total Environ. 2021 Jun 01;771:145417. doi: 10.1016/j.scitotenv.2021.145417. Epub 2021 Jan 27.

Multimetric index (MMI) approach is a broadly used in ecological assessment because it can integrate information of various kinds of ecologically related metrics of freshwater ecosystems and provide an easily understandable score for purpose of further evaluation and managements....

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Liu B, Li D, Chen S, et al. Improving biological condition assessment accuracy by multimetric index approach with microalgae in streams and lakes. Sci Total Environ. 2021;771:145417doi: 10.1016/j.scitotenv.2021.145417.
Liu, B., Li, D., Chen, S., Wu, N., & Guan, Y. (2021). Improving biological condition assessment accuracy by multimetric index approach with microalgae in streams and lakes. The Science of the total environment, 771145417. https://doi.org/10.1016/j.scitotenv.2021.145417
Liu, Bo, et al. "Improving biological condition assessment accuracy by multimetric index approach with microalgae in streams and lakes." The Science of the total environment vol. 771 (2021): 145417. doi: https://doi.org/10.1016/j.scitotenv.2021.145417
Liu B, Li D, Chen S, Wu N, Guan Y. Improving biological condition assessment accuracy by multimetric index approach with microalgae in streams and lakes. Sci Total Environ. 2021 Jun 01;771:145417. doi: 10.1016/j.scitotenv.2021.145417. Epub 2021 Jan 27. PMID: 33736158.
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Showing 25 to 36 of 293 entries