A novel indicator for defining plain urban river network cyanobacterial blooms: resource use efficiency.

Increasing eutrophication and climate change have led to heavy cyanobacterial blooms in water diversion sources (e.g., lakes, reservoirs), which can potentially cause algae-bearing water to spread to downstream to an urban river network via diversion channels. Defining the extent of cyanobacterial blooms in an urban river network has become a novel concern in urban river management. In this paper, we investigated the physicochemical and algae community characteristics of a small, closed, urban river network, JiangXinZhou (JXZ), in the Lake Taihu basin. We propose a novel indicator, resource use efficiency (RUE), for defining the extent of cyanobacterial blooms in JXZ, whose recreational drinking water comes entirely from outside diversion sources. The results show that the JXZ's aquatic habitat conditions (mean water temperature, total nitrogen concentration, total phosphorus concentration, and nitrogen to phosphorus ratio) are highly suitable for the proliferation of cyanobacterial biomass during the high-water period. The RUE was used for calculation and shows a strong relationship with algae density, which means that it can be used as an index to define the degree of urban river cyanobacterial blooms. The findings indicate that the risk of cyanobacterial bloom is absent when the RUE is less than 46.81; blooms appear in the water bodies when the RUE reaches up to 106.68. This work provides theoretical support for the sustainable use of regional water resources.

Two hybrid data-driven models for modeling water-air temperature relationship in rivers.

River water temperature (RWT) forecasting is important for the management of stream ecology. In this paper, a new method based on coupling of wavelet transformation (WT) and artificial intelligence (AI) techniques, including multilayer perceptron neural network (MLPNN) and adaptive neural-fuzzy inference system (ANFIS) for RWT prediction is proposed. The performances of the hybrid models are compared with regular MLPNN and ANFIS models and multiple linear regression (MLR) models for RWT forecasting in two river stations in the Drava River, Croatia. Model performance was evaluated using the coefficient of correlation (R), the Willmott index of agreement (d), the root mean squared error (RMSE), and the mean absolute error (MAE). Results indicate that the combination of WT and AI models (WTMLPNN and WTANFIS) yield better models than the conventional forecasting models for RWT simulation for both regular periods and heatwave events. The MLPNN and ANFIS models outperform the MLR models for RWT simulation for the studied river stations. RMSE values of WTMLPNN2 and WTANFIS2 models range from 1.127 to 1.286 °C, and 1.216 to 1.491 °C for the Botovo and Donji Miholjac stations respectively. Additionally, modeling results further confirm the importance of the day of year (DOY) on the thermal dynamics of the river. The results of this study indicate the potential of coupling of WT and MLPNN, ANFIS models in forecasting RWT.