Assessment of Lake Water Quality and Eutrophication Risk in an Agricultural Irrigation Area: A Case Study of the Chagan Lake in Northeast China

Artificial Neural Networks (ANNs) have wide applications in aquatic ecology and specifically in modelling water quality and biotic responses to environmental predictors. However, data scarcity is a common problem that raises the need to optimize modelling approaches to overcome data limitations. With this paper, we investigate the optimal k-fold cross validation in building an ANN using a small water-quality data set. The ANN was created to model the chlorophyll-a levels of a shallow eutrophic lake (Mikri Prespa) located in N. Greece. The typical water quality parameters serving as the ANN’s inputs are pH, dissolved oxygen, water temperature, phosphorus, nitrogen, electric conductivity, and Secchi disk depth. The available data set was small, containing only 89 data samples. For that reason, k-fold cross validation was used for training the ANN. To find the optimal k value for the k-fold cross validation, several values of k were tested (ranging from 3 to 30). Additionally, the leave-one-out (LOO) cross validation, which is an extreme case of the k-fold cross validation, was also applied. The ANN’s performance indices showed a clear trend to be improved as the k number was increased, while the best results were calculated for the LOO cross validation as expected. The computational times were calculated for each k value, where it was found the computational time is relatively low when applying the more expensive LOO cross validation; therefore, the LOO is recommended. Finally, a sensitivity analysis was examined using the ANN to investigate the interactions of the input parameters with the Chlorophyll-a, and hence examining the potential use of the ANN as a water management tool for nutrient control.

Section: Discussionmentioning

confidence: 99%

Modelling Freshwater Eutrophication with Limited Limnological Data Using Artificial Neural Networks

Hadjisolomou

Stefanidis

Herodotou

et al. 2021

“…The lake is surrounded by three irrigation districts, including an old irrigation district (Qianguo district) and two new irrigation districts (Da'an and Qian'an districts) covering 5.07 × 10 4 , 2.19 × 10 4 and 1.33 × 10 4 hectares, respectively (Figure 1). As the lake receives discharge from the Qianguo and Da'an irrigation districts, nutrients, salt, and alkali flowing into the lake have caused eutrophication and salinity to increase [61,62]. According to the development plan of the Songyuan irrigation district, 'the irrigation discharge from the Qian'an irrigation district will flow into Chagan Lake in the future.…”

Section: Study Areamentioning

confidence: 99%

“…Based on this classification, Chagan Lake may eventually evolve from a freshwater lake to sub-saline lake, similar to the process of salinization of the Boston Lake [28,51]. However, the depth of Chagan Lake is less than Boston Lake [51,61], hence the ecosystem of Chagan Lake is more vulnerable to water salinization. The fish community structure and production are limited by the increased salinity of the lake because higher salinity can inhibit fish embryos from hatching and can reduce plankton production [90,93].…”

Section: The Effect Of Salinization On Lake Ecosystem Healthmentioning

confidence: 99%

“…A number of short-term and long-term salinity management strategies have been developed around the world [94,95], promoting the balance of regional ecology and the economy. Previous studies have shown that conflicts of interest between ecological health of the lake and its surrounding irrigation areas of development exist worldwide [6,61,62,96]. Accelerating the lake water circulation and decreasing the salinity loading from the input water are two effective methods to maintain the salinity below the threshold [97].…”

Section: Strategy For Sustainable Lake Water Management To Control Salinizationmentioning

confidence: 99%

See 1 more Smart Citation

Effects of Irrigation Discharge on Salinity of a Large Freshwater Lake: A Case Study in Chagan Lake, Northeast China

Liu

Zhang

et al. 2020

Self Cite

The salinization of freshwater lakes by agricultural activities poses a threat to many lake ecosystems around the world. Quantitative, medium- to long-term studies are needed to understand how some common agricultural practices, such as the discharge of crop irrigation in the vicinities of large lakes, may affect lake salinization. In this study, hydrological, hydrodynamics, water quality and meteorological datasets were used to analyze the long-term spatial-temporal variations of water salinities of a major lake, the Chagan Lake, in Northeast China. An integrated hydrodynamics-salinity model was used to simulate lake water salinity changes taking place at different times and locations, including (i) salt accumulations during a non-frozen period, and (ii) the time when water salinity may reach a significant threshold (1 psu) that jeopardizes a major environmental and economic value of this lake (i.e., the cultivation of local fish species). The results confirmed that Chagan Lake was indeed undergoing salinization in the ten year period between 2008 and 2018. The spatial-temporal patterns of the salinization processes were identified. For instance, (i) the mean salinity of the lake water was found to be 0.55 psu in the summer season of the region and 0.53 psu in the winter, and (ii) between May to October the salinity was up to 0.62 psu in the western region of the lake. The rate of salt accumulation was found to be 97 ton per annum during the non-frozen period. The simulation predicted that by 2024 the lake water will become sub-saline (salinity > 1.07 psu) which is toxic to fish species, if the current practice of irrigation discharge into the lake continues. In the scenario that the amount of irrigation discharges into the lake doubles, the western region of the lake will become sub-saline within one year, and then the whole lake within three years. Overall, this study has produced results that are useful to authorities around the world, for balancing the risks and benefits of developing crop irrigation fields in areas surrounding large freshwater lakes.

“…Chagan Lake is located in the western part of Jilin Province, which is the drainage area for the commodity grain production base. Drainage from the surrounding irrigation fields is the major water source of the lake, accounting for 63.4% of all water sources [20]. According to the Special Plan for Ecological and Environmental Protection of the Chagan Lake Basin, the area of irrigated high-standard farmland in Jilin Province has continued to grow since 2006, and the intensity of water supply and drainage in the irrigation area has been continuously increasing.…”

Section: Study Areamentioning

confidence: 99%

Application of a New Improved Weighting Method, ESO Method Combined with Fuzzy Synthetic Method, in Water Quality Evaluation of Chagan Lake

Zhao

Xiao

Chai

et al. 2021

The existing weighting methods mainly comprise subjective and objective weighting and have a certain degree of subjectivity, with certain requirements for the professional ability of the users and unstable results. Therefore, an improved weighting method based on the entropy weight, over-standard multiple, and single-factor evaluation methods, referred to as the ESO method, is proposed. The advantages and advancements of the ESO method are demonstrated in this study by combining it with the fuzzy synthetic evaluation method to evaluate the water quality of Chagan Lake wetland from 2007 to 2016. The main conclusions of this study are as follows: 1. The ESO method has more comprehensive consideration factors, lower requirements for the professional ability of users, and more stable weighting results than the traditional weighting method. Therefore, it is highly suitable for beginners and frontline staff who are not professionally qualified and cannot accurately conduct subjective weighting. Meanwhile, owing to the amendment rule and emphasis on the local weight of the sample in the ESO method, it is applicable to time-series samples. 2. The ESO method better allocates the amendment weights to indicators with a higher degree of pollution; thus, the final comprehensive evaluation results are relatively conservative. However, in contrast to the single-factor evaluation, the conservatism of ESO method is the result of the comprehensive effect of all samples; thus, the conservative result of the ESO method is more reasonable. 3. The water quality of Chagan Lake in 2009 and 2015 was class IV, which did not meet the standard, while that in remaining the eight years was class III, which met the requirements of the national 13th Five-Year Plan. The results of this study can provide a new approach to weighting calculation methods and a basis for the protection and treatment of the ecological environment of the Chagan Lake wetland.