Kun Shi

Aquatic vegetation in response to increased eutrophication and degraded light climate in Eastern Lake Taihu: Implications for lake ecological restoration

Terrestrial and aquatic ecosystem degradation is widely recognized as a major global environmental and development problem. Although great efforts have been made to prevent aquatic ecosystem degradation, the degree, extent and impacts of this phenomenon remain controversial and unclear, such as its driving mechanisms. Here, we present results from a 17-year field investigation (1998–2014) of water quality and a 12-year remote sensing mapping (2003–2014) of the aquatic vegetation presence frequency (VPF) in Eastern Lake Taihu, a macrophyte-dominated bay of Lake Taihu in China. In the past 17 years, nutrient concentrations and water level (WL) have significantly increased, but the Secchi disk depth (SDD) has significantly decreased. These changes were associated with increased lake eutrophication and a degraded underwater light climate that further inhibited the growth of aquatic vegetation. In Eastern Lake Taihu, increased nutrients, chlorophyll a and WL, and a decreased SDD were all significantly correlated with a decreased VPF. NH4+-N concentration and SDD/WL were the most important controlling factors for VPF. Therefore, increased anthropogenic nutrient inputs and a degraded underwater light climate surely result in a decreased VPF. These results elucidate the driving mechanism of aquatic vegetation degradation and will facilitate Lake Taihu ecological restoration.

Dissolved oxygen stratification and response to thermal structure and long-term climate change in a large and deep subtropical reservoir (Lake Qiandaohu, China).

From January 2010 to March 2014, detailed depth profiles of water temperature, dissolved oxygen (DO), and chromophoric dissolved organic matter (CDOM) were collected at three sites in Lake Qiandaohu, a large, deep subtropical reservoir in China. Additionally, we assessed the changes in DO stratification over the past 61 years (1953-2013) based on our empirical models and long-term air temperature and transparency data. The DO concentration never fell below 2 mg/L, the critical value for anoxia, and the DO depth profiles were closely linked to the water temperature depth profiles. In the stable stratification period in summer and autumn, the significant increase in CDOM in the metalimnion explained the decrease in DO due to the oxygen consumed by CDOM. Well-developed oxygen stratification was detected at the three sites in spring, summer and autumn and was associated with thermal stratification. Oxycline depth was significantly negatively correlated with daily air temperature and thermocline thickness but significantly positively correlated with thermocline depth during the stratification weakness period (July-February). However, there were no significant correlations among these parameters during the stratification formation period (March-June). The increase of 1.67 °C in yearly average daily air temperature between 1980 and 2013 and the decrease of 0.78 m in Secchi disk depth caused a decrease of 1.65 m and 2.78 m in oxycline depth, respectively, facilitating oxygen stratification and decreasing water quality. Therefore, climate warming has had a substantial effect on water quality through changing the DO regime in Lake Qiandaohu.

The influence of changes in wind patterns on the areal extension of surface cyanobacterial blooms in a large shallow lake in China.

It has been hypothesized that climate change will induce the areal extension of cyanobacterial blooms. However, this hypothesis lacks field-based observation. In the present study both long-term historical data and short-term field measurement were used to identify the importance of changes in wind patterns on the cyanobacterial bloom in Lake Taihu (China), a large, shallow, eutrophic lake located in a subtropical zone. The cyanobacterial bloom mainly composed of Microcystis spp. recurred frequently throughout the year. The regression analysis of multi-year satellite image data extracted by the Floating Algae Index revealed that both the annual mean monthly maximum cyanobacterial bloom area (MMCBA) increased year by year from 2000 to 2011, while the contemporaneous cyanobacterial biomass showed no significant change. However, the correlation analysis shows that MMCBA was negatively correlated with wind speed. Our short-term field measurements indicated that the influence of wind on surface cyanobacterial blooms is that the Chlorophyll-a (Chla) concentration is fully mixing throughout the water column when the wind speed exceed 7 m s(-1). At lower wind speeds, there was vertical stratification of Chla with high surface concentrations and an increase in bloom area. The regression analysis of wind speed indicates that the climate has changed over the last decade. Lake Taihu has become increasingly calm, with the decrease of strong wind frequency between 2000 and 2011, corresponding to the increase in the MMCBA over time. Therefore, we conclude that changes in wind patterns related to climate change have favored the increase of cyanobacterial blooms in Lake Taihu.

Long-Term Satellite Observations of Microcystin Concentrations in Lake Taihu during Cyanobacterial Bloom Periods.

Microcystins (MCs) produced by cyanobacteria pose a serious threat to public health. Intelligence on MCs distributions in freshwater is therefore critical for environmental agencies, water authorities, and public health organizations. We developed and validated an empirical model to quantify MCs in Lake Taihu during cyanobacterial bloom periods using the atmospherically Rayleigh-corrected moderate resolution imaging spectroradiometer (MODIS-Aqua) (Rrc) products and in situ data by means of chlorophyll a concentrations (Chla). First, robust relationships were constructed between MCs and Chla (r = 0.91; p < 0.001; t-test) and between Chla and a spectral index derived from Rrc (r = -0.86; p < 0.05; t-test). Then, a regional algorithm to analyze MCs in Lake Taihu was constructed by combining the two relationships. The model was validated and then applied to an 11-year series of MODIS-Aqua data to investigate the spatial and temporal distributions of MCs. MCs in the lake were markedly variable both spatially and temporally. Cyanobacterial bloom scums, temperature, wind, and light conditions probably affected the temporal and spatial distribution of MCs in Lake Taihu. The findings demonstrate that remote sensing reconnaissance in conjunction with in situ monitoring can greatly aid MCs assessment in freshwater.

Dissolved organic matter fluorescence at wavelength 275/342 nm as a key indicator for detection of point-source contamination in a large Chinese drinking water lake

Surface drinking water sources have been threatened globally and there have been few attempts to detect point-source contamination in these waters using chromophoric dissolved organic matter (CDOM) fluorescence. To determine the optimal wavelength derived from CDOM fluorescence as an indicator of point-source contamination in drinking waters, a combination of field campaigns in Lake Qiandao and a laboratory wastewater addition experiment was used. Parallel factor (PARAFAC) analysis identified six components, including three humic-like, two tryptophan-like, and one tyrosine-like component. All metrics showed strong correlation with wastewater addition (r(2) > 0.90, p < 0.0001). Both the field campaigns and the laboratory contamination experiment revealed that CDOM fluorescence at 275/342 nm was the most responsive wavelength to the point-source contamination in the lake. Our results suggest that pollutants in Lake Qiandao had the highest concentrations in the river mouths of upstream inflow tributaries and the single wavelength at 275/342 nm may be adapted for online or in situ fluorescence measurements as an early warning of contamination events. This study demonstrates the potential utility of CDOM fluorescence to monitor water quality in surface drinking water sources.

Remote chlorophyll-a estimates for inland waters based on a cluster-based classification.

Accurate estimates of chlorophyll-a concentration (Chl-a) from remotely sensed data for inland waters are challenging due to their optical complexity. In this study, a framework of Chl-a estimation is established for optically complex inland waters based on combination of water optical classification and two semi-empirical algorithms. Three spectrally distinct water types (Type I to Type III) are first identified using a clustering method performed on remote sensing reflectance (R(rs)) from datasets containing 231 samples from Lake Taihu, Lake Chaohu, Lake Dianchi, and Three Gorges Reservoir. The classification criteria for each optical water type are subsequently defined for MERIS images based on the spectral characteristics of the three water types. The criteria cluster every R(rs) spectrum into one of the three water types by comparing the values from band 7 (central band: 665 nm), band 8 (central band: 681.25 nm), and band 9 (central band: 708.75 nm) of MERIS images. Based on the water classification, the type-specific three-band algorithms (TBA) and type-specific advanced three-band algorithm (ATBA) are developed for each water type using the same datasets. By pre-classifying, errors are decreased for the two algorithms, with the mean absolute percent error (MAPE) of TBA decreasing from 36.5% to 23% for the calibration datasets, and from 40% to 28% for ATBA. The accuracy of the two algorithms for validation data indicates that optical classification eliminates the need to adjust the optimal locations of the three bands or to re-parameterize to estimate Chl-a for other waters. The classification criteria and the type-specific ATBA are additionally validated by two MERIS images. The framework of first classifying optical water types based on reflectance characteristics and subsequently developing type-specific algorithms for different water types is a valid scheme for reducing errors in Chl-a estimation for optically complex inland waters.

Lake Topography and Wind Waves Determining Seasonal-Spatial Dynamics of Total Suspended Matter in Turbid Lake Taihu, China: Assessment Using Long-Term High-Resolution MERIS Data

Multiple comprehensive in situ bio-optical investigations were conducted from 2005 to 2010 and covered a large variability of total suspended matter (TSM) in Lake Taihu to calibrate and validate a TSM concentration estimation model based on Medium Resolution Imaging Spectrometer (MERIS) data. The estimation model of the TSM concentration in Lake Taihu was developed using top-of-atmosphere (TOA) radiance of MERIS image data at band 9 in combination with a regional empirical atmospheric correction model, which was strongly correlated with the in situ TSM concentration (r 2 = 0.720, p<0.001, and n = 73). The relative root mean square error (RRMSE) and mean relative error (MRE) were 36.9% and 31.6%, respectively, based on an independent validation dataset that produced reliable estimations of the TSM concentration. The developed algorithm was applied to 50 MERIS images from 2003 to 2011 to obtain a high spatial and temporal heterogeneity of TSM concentrations in Lake Taihu. Seasonally, the highest and lowest TSM concentrations were found in spring and autumn, respectively. Spatially, TSM concentrations were high in the southern part and center of the lake and low in Xukou Bay, East Lake Taihu. The lake topography, including the water depth and distance from the shore, had a significant effect on the TSM spatial distribution. A significant correlation was found between the daily average wind speed and TSM concentration (r 2 = 0.685, p<0.001, and n = 50), suggesting a critical role of wind speed in the TSM variations in Lake Taihu. In addition, a low TSM concentration was linked to the appearance of submerged aquatic vegetation (SAV). Therefore, TSM dynamics were controlled by the lake topography, wind-driven sediment resuspension and SAV distribution.

Hyperspectral determination of eutrophication for a water supply source via genetic algorithm-partial least squares (GA-PLS) modeling

Morse Reservoir (MR), a major source of the water supply for the Indianapolis metropolitan region, is now experiencing nuisance cyanobacterial blooms. These blooms cause water quality degradation, as well as reducing the aesthetic quality of water by producing toxins, scums, and foul odors. Hyperspectral remote sensing data from both in situ and airborne AISA measurements were applied to GA-PLS by relating the spectral signal with measured water eutrophication parameters, e.g., chlorophyll-a (Chl-a), phycocyanin (PC), total suspended matter (TSM), and Secchi disk depth (SDD). Our results indicate that GA-PLS relating field sensor acquired spectral reflectance to the above-mentioned four parameters yielded low root mean square error between measured and estimated Chl-a (RMSE=10.4; Range (R): 1.8-215.8 μg/L), PC (RMSE=18.6; R: 1.4-371.0 μg/L), TSM (RMSE=3.8; R: 3.6-81.4 mg/L), SDD (RMSE=5.8; R: 25-135 cm) for MR. The GA-PLS model also yielded high performance with AISA image spectra, and the RMSEs were 12.1 μg/L, 25.3 μg/L, 5.9 mg/L and 5.7 cm, respectively for Chl-a, PC, TSM, and SDD. Four water quality parameters were mapped with GA-PLS using AISA hyperspectral image. Based on these results, in situ and airborne hyperspectral remote sensors can provide both quantitative and qualitative information on the distribution and concentration of cyanobacteria, suspended matter, and transparency in MR.

Long-term MODIS observations of cyanobacterial dynamics in Lake Taihu: Responses to nutrient enrichment and meteorological factors

We developed and validated an empirical model for estimating chlorophyll a concentrations (Chla) in Lake Taihu to generate a long-term Chla and algal bloom area time series from MODIS-Aqua observations for 2003 to 2013. Then, based on the long-term time series data, we quantified the responses of cyanobacterial dynamics to nutrient enrichment and climatic conditions. Chla showed substantial spatial and temporal variability. In addition, the annual mean cyanobacterial surface bloom area exhibited an increasing trend across the entire lake from 2003 to 2013, with the exception of 2006 and 2007. High air temperature and phosphorus levels in the spring can prompt cyanobacterial growth, and low wind speeds and low atmospheric pressure levels favor cyanobacterial surface bloom formation. The sensitivity of cyanobacterial dynamics to climatic conditions was found to vary by region. Our results indicate that temperature is the most important factor controlling Chla inter-annual variability followed by phosphorus and that air pressure is the most important factor controlling cyanobacterial surface bloom formation followed by wind speeds in Lake Taihu.

A semi-analytical algorithm for remote estimation of phycocyanin in inland waters

Phycocyanin (PC) is the unique and important accessory pigment for monitoring toxic cyanobacteria in inland waters. In this study, a semi-analytical algorithm combining both three band indices and a baseline algorithm (TBBA) was developed to estimate PC concentrations and then tested in three eutrophic and turbid reservoirs. TBBA does not need to optimize wavelengths as either the traditional baseline algorithm or three-band algorithms does when it is used across different study sites. TBBA evidently corrects some effects of absorptions due to colored detritus matter and other pigments and backscattering of water column. TBBA accurately estimated PC concentrations with R(2)=0.8573 and rRMSE=31.4% for water samples with the PC range from 1.4 mgm(-3) to 146.1 mgm(-3). Particularly, TBBA outperformed three-band algorithms and a previously proposed semi-empirical algorithm for the prediction of low PC (PC ≤ 50 mgm(-3)) concentration. Further analysis reveals that both the variations of PC:Chl-a and PC:TSM are important factors influencing the performance of all PC algorithms examined in this study and more efforts are required to improve the performance of TBBA on water samples with low PC concentration.