Lake Xingyun is a hypertrophic shallow lake on the Yunnan Plateau of China. Its water quality (WQ) has degraded severely during the past three decades with catchment development. To better understand the external nutrient loading impacts on WQ, we measured nutrient concentrations in the main tributaries during January 2010–April 2018 and modelled the monthly volume of all the tributaries for the same period. The results show annual inputs of total nitrogen (TN) had higher variability than total phosphorus (TP). The multi-year average load was 183.8 t/year for TN and 23.3 t/year for TP during 2010–2017. The average TN and TP loads for 2010–2017 were 36.6% higher and 63.8% lower, respectively, compared with observations in 1999. The seasonal patterns of TN and TP external loading showed some similarity, with the highest loading during the wet season and the lowest during the dry season. Loads in spring, summer, autumn, winter, and the wet season (May–October) accounted for 14.2%, 48.8%, 30.3%, 6.7%, and 84.9% of the annual TN load and 14.1%, 49.8%, 28.1%, 8%, and 84.0% of the annual TP load during 2010–2017. In-lake TN and TP concentrations followed a pattern similar to the external loading. The poor correlation between in-lake nutrient concentrations and tributary nutrient inputs at monthly and annual time scales suggests both external loading and internal loading were contributing to the lake eutrophication. Although effective lake restoration will require reducing nutrient losses from catchment agriculture, there may be a need to address a reduction of internal loads through sediment dredging or capping, geochemical engineering, or other effective measures. In addition, the method of producing monthly tributary inflows based on rainfall data in this paper might be useful for estimating runoff at other lakes.
There are many rivers flowing from complex paths into Lake Dianchi. At present, there is a lack of inflow and water quality monitoring data for some rivers, resulting in limited accuracy of statistical results regarding water volume and external loading estimations. In this study, we used DYRESM to estimate the water volume entering Waihai of Lake Dianchi from 2007 to 2019 without historical hydrological observation data. Then, we combined this information with the monthly monitoring data of water quality to calculate the annual external loading. Our results showed that: (1) DYRESM could effectively capture the extreme changes of water level at Waihai, showing its reliable applicability to Lake Dianchi. (2) The average annual inflow of rivers entering Waihai was about 6.69 × 108 m3. The fitting relationship between river inflow and precipitation was significant on annual scale (r = 0.74), with a higher inner-annual fitting coefficient between them (r = 0.98), thus suggesting that precipitation and its caused river inflows are the main water source for Waihai. (3) From 2007 to 2010, the river loadings remained at a high level. They decreased to 2445.44 t (total nitrogen, TN) and 106.53 t (total phosphorus, TP) due to a followed drought in 2011. (4) The river loading had annual variation characteristics. The contribution rates of TN and TP loading in the rainy season were 63% and 67% respectively. (5) Panlong River, Daqing River, Jinjia River, Xinbaoxiang River, Cailian River and Hai River were the main inflow rivers. Their loadings accounted for 81.3% (TN) and 80.3% (TP) of the total inputs. (6) River loadings have gradually reduced and the water quality of Waihai has continually improved. However, Pearson analysis results showed that the water quality parameters were not significantly correlated with their corresponding external loading at Waihai, indicating that there might be other factors influencing the water quality. (7) The contribution rates of internal release to the total loads of TN and TP at Waihai were estimated to be 7.6% and 8.9% respectively, suggesting that the reductions of both external and internal loading should be considered in order to significantly improve the water quality at Waihai of Lake Dianchi.
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