Abstract
For the assessment of lake’s eutrophication status, a great deal of data uncertainties exists under the circumstances that monitoring data often are scarce and inaccuracy or the variation intervals are wide. In order to process these uncertainties of data and provide more valuable information for the decision makers, a methodology for assessing the eutrophication status was established by coupling Monte Carlo and triangular fuzzy numbers approaches and further combining it with the trophic level index method. This developed methodology was illustrated by a case study of evaluating the eutrophication status of Dongting Lake in Mid-South China (Hunan Province). The results indicated that the quantitative information of possible intervals of trophic level index, their corresponding probabilities and the comprehensive eutrophication statuses can be obtained. The eutrophication status of the East Dongting Lake was more serious than the southern and western parts. Portions of both East and South Dongting Lake showed a greater probability to light-eutrophic status, but with a worsening tendency, i.e., becoming mid-eutrophic in the 2010 year. By processing the data fuzzily and simulating their distribution characteristics stochastically, the presented methodology can be employed to process the uncertainties of the data evaluation and obtain a better early detection/warning of eutrophication levels with less requirement of time. Therefore, more reliable/valuable information can be provided to the decision makers, e.g., lake management authorities.
This is a preview of subscription content, log in via an institution to check access.
Note: Satellite image is sourced from ArcGIS Desktop Explorer
Similar content being viewed by others
References
Aizaki M (1981) Application of modified Carlson’s trophic state index to Japanese lakes and its relationships to other parameters related to trophic state (in Japanese with English summary). Res Rep Natl Inst Environ Stud Jpn 23:13–31
Borsuk ME, Stow CA, Reckhow KH (2004) A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis. Ecol Model 173:219–239. doi:10.1016/j.ecolmodel.2003.08.020
Carlson RE (1977) A trophic state index for lakes. Limnol Oceanogr 22:361–369
CEMS (2001) Lakes (reservoirs) Eutrophication Assessment methods and classification technology requirements. China Environmental Monitoring Station, Beijing
Chen C et al (2015) Challenges and opportunities of German-Chinese cooperation in water science and technology. Environ Earth Sci 73:4861–4871. doi:10.1007/s12665-015-4149-5
Cheung KC, Poon BHT, Lan CY, Wong MH (2003) Assessment of metal and nutrient concentrations in river water and sediment collected from the cities in the Pearl River Delta, South China. Chemosphere 52:1431–1440. doi:10.1016/S0045-6535(03)00479-X
Dahiya S, Singh B, Gaur S, Garg VK, Kushwaha HS (2007) Analysis of groundwater quality using fuzzy synthetic evaluation. J Hazard Mater 147:938–946. doi:10.1016/j.jhazmat.2007.01.119
Devlin M, Bricker S, Painting S (2011) Comparison of five methods for assessing impacts of nutrient enrichment using estuarine case studies. Biogeochemistry 106:177–205. doi:10.1007/s10533-011-9588-9
Ding X, Li X (2011) Monitoring of the water-area variations of Lake Dongting in China with ENVISAT ASAR images. Int J Appl Earth Obs Geoinf 13:894–901
Dou C, Woldt W, Bogardi I, Dahab M (1995) Steady state groundwater flow simulation with imprecise parameters. Water Resour Res 31:2709–2719
Elith J, Burgman MA, Regan HM (2002) Mapping epistemic uncertainties and vague concepts in predictions of species distribution. Ecol Model 157:313–329
Frumin GT, Khuan Z-Z (2012) Trophic status of fresh-water lakes in China. Russ J Gen Chem 81:2653–2657. doi:10.1134/s1070363211130044
Gardner MJ, Altman DG (1986) Confidence intervals rather than P values: estimation rather than hypothesis testing. Br Med J 292:746–750. doi:10.1136/bmj.292.6522.746
Huang S, Li J, Xu M (2012) Water surface variations monitoring and flood hazard analysis in Dongting Lake area using long-term Terra/MODIS data time series. Nat Hazards 62:93–100
Huang DZ, Wan Q, Li LQ, Wang T, Lu SY, Ou FP, Tian Q (2013) Changes of water quality and eutrophic state in recent 20 years of Dongting Lake. Res Environ Sci 26:27–33 (in Chinese)
Huang J et al (2015) Long-term variations of TN and TP in four lakes fed by Yangtze River at various timescales. Environ Earth Sci 74:3993–4009. doi:10.1007/s12665-015-4714-y
Icaga Y (2007) Fuzzy evaluation of water quality classification. Ecol Ind 7:710–718. doi:10.1016/j.ecolind.2006.08.002
James F (1980) Monte Carlo theory and practice. Rep Prog Phys 43:1145
Kahraman C, Kaya İ (2008) Fuzzy process capability indices for quality control of irrigation water. Stoch Environ Res Risk Assess 23:451–462. doi:10.1007/s00477-008-0232-8
Karydis M (1996) Quantitative assessment of eutrophication: a scoring system for characterising water quality in coastal marine ecosystems. Environ Monit Assess 41:233–246
Keith DA (2009) The interpretation, assessment and conservation of ecological communities. Ecol Manag Restor 10:3–15
Liu W, Li S, Bu H, Zhang Q, Liu G (2011) Eutrophication in the Yunnan Plateau lakes: the influence of lake morphology, watershed land use, and socioeconomic factors. Environ Sci Pollut Res 19:858–870. doi:10.1007/s11356-011-0616-z
Lourenço RW, Landim PMB, Rosa AH, Roveda JAF, Martins ACG, Fraceto LF (2009) Mapping soil pollution by spatial analysis and fuzzy classification. Environ Earth Sci 60:495–504. doi:10.1007/s12665-009-0190-6
Mpimpas H, Anagnostopoulos P, Ganoulis J (2001) Modelling of water pollution in the Thermaikos Gulf with fuzzy parameters. Ecol Model 142:91–104
Müller B, Berg M, Yao ZP, Zhang XF, Wang D, Pfluger A (2008) How polluted is the Yangtze river? Water quality downstream from the Three Gorges Dam. Sci Total Environ 402:232–247
Nakayama T, Shankman D (2013) Impact of the Three-Gorges Dam and water transfer project on Changjiang floods. Glob Planet Change 100:38–50
Nazir M, Khan FI (2006) Human health risk modeling for various exposure routes of trihalomethanes (THMs) in potable water supply. Environ Model Softw 21:1416–1429. doi:10.1016/j.envsoft.2005.06.009
Ronald EG, Robert EY (1997a) Analysis of the error in the standard approximation used for multiplication of triangular and trapezoidal fuzzy numbers and the development of a new approximation. Fuzzy Sets Syst 91:1–13
Ronald EG, Robert EY (1997b) A parametric representation of fuzzy numbers and their arithmetic operators. Fuzzy Sets Syst 91:185–202
Uricchio VF, Giordano R, Lopez N (2004) A fuzzy knowledge-based decision support system for groundwater pollution risk evaluation. J Environ Manag 73:189–197. doi:10.1016/j.jenvman.2004.06.011
Wang LQ, Liang T (2016) Distribution patterns and dynamics of phosphorus forms in the overlying water and sediment of Dongting Lake. J Great Lakes Res 42:565–570
Wang S, Zheng B, Chen C, Dohmann M, Kolditz O (2015) Thematic issue: water of the Erhai and Dianchi Lakes. Environ Earth Sci 74:3685–3688. doi:10.1007/s12665-015-4727-6
Wu G, Liu L, Chen F, Fei T (2014) Developing MODIS-based retrieval models of suspended particulate matter concentration in Dongting Lake, China. Int J Appl Earth Obs Geoinf 32:46–53
Xi B, Su J, Sun Y, Huo S, Zheng B, Tiehm A, Kolditz O (2015) Thematic issue: water of the Taihu Lake. Environ Earth Sci 74:3929–3933. doi:10.1007/s12665-015-4732-9
Yang B, Liu Y, Ou F, Yuan M (2011) Temporal and spatial analysis of cod concentration in East Dongting Lake by using of remotely sensed data. Procedia Environ Sci 10:2703–2708
Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353
Zhang J, Xu K, Yang Y, Qi L, Hayashi S, Watanabe M (2006) Measuring water storage fluctuations in Lake Dongting, China, by Topex/Poseidon satellite altimetry. Environ Monit Assess 115:23–37. doi:10.1007/s10661-006-5233-9
Zhao S, Fang J, Miao S, Gu B, Tao S, Peng C, Tang Z (2005) The 7-decade degradation of a large freshwater lake in Central Yangtze River, China. Environ Sci Technol 39:431–436. doi:10.1021/es0490875
Zou R, Lung WS (2000) Uncertainty analysis for a dynamic phosphorus model with fuzzy parameters. Water Qual Ecosyst Model 1:237–252
Acknowledgments
This research was financially supported by the National Natural Science Foundation of China (51039001, 51479072), the Fundamental Research Funds for the Central Universities and the Hunan Provincial Natural Science Foundation of China (13JJB002) and BMBF CLIENT project “Managing Water Resources for Urban Catchments” in the framework of the Sino–German “Innovation Cluster Major Water” (Grant No. 02WCL1337A) (Dohmann et al. 2016).
Author information
Authors and Affiliations
Corresponding authors
Additional information
This article is part of a Topical Collection in Environmental Earth Sciences on ‘‘Environment and Health in China II’’, guest edited by Tian-Xiang Yue, Cui Chen, Bing Xu and Olaf Kolditz.
Rights and permissions
About this article
Cite this article
Zhi, G., Chen, Y., Liao, Z. et al. Comprehensive assessment of eutrophication status based on Monte Carlo–triangular fuzzy numbers model: site study of Dongting Lake, Mid-South China. Environ Earth Sci 75, 1011 (2016). https://doi.org/10.1007/s12665-016-5819-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12665-016-5819-7