In this article, a data matrix of 20 indicators (6960 observations) was obtained from 29 water quality monitoring stations of the Middle Route (MR) of the South-to-North Water Diversion Project of China (SNWDPC). Multivariate statistical techniques including analysis of variance (ANOVA), correlation analysis (CA), and principal component analysis (PCA) were applied to understand and identify the interrelationships between the different indicators and the most contributive sources of anthropogenic and natural impacts on water quality. The water quality index (WQI) was used to assess the classification and variation of water quality. The distributions of the indicators revealed that six heavy-metal indicators including arsenic (As), mercury (Hg), cadmium (Cd), chromium (Cr), selenium (Se), and lead (Pb) were within the Class I standard, while the As, Pb, and Cd displayed spatial variation. Moreover, some physicochemical indicators such as dissolved oxygen, 5-day biochemical oxygen demand (as BOD₅), and total phosphorus (TP) had spatio-temporal variability. The correlation analysis result demonstrated that As, Hg, Cd, Cr, Se, Pb, copper (Cu), and zinc (Zn) had high correlation coefficients. The PCA result extracted three principal components (PC) accounting for 82.67% of the total variance, while the first PC was indicative of the mixed sources of anthropogenic and natural contributions, the second and the third PCs were mainly controlled by human activities and natural sources, respectively. The calculation results of the WQI showed an excellent water quality of the MR of the SNWDPC where the values of the stations ranged from 10.49 to 17.93, while Hg was the key indicator to determine the WQI > 20 of six stations, which indicated that the Hg can be the main potential threat to water quality and human health in this project. The result suggests that special attention should be paid to the monitoring of Hg, and the investigation and supervision within the areas of high-density human activities in this project should be taken to control the impacts of urban and industrial production and risk sources on water quality.

Water quality forecast is a critical part of water security management. Spatiotemporal and multifactorial variations make water quality very complex and changeable. In this article, a novel model, which was based on back propagation neural network that was optimized by the Cuckoo Search algorithm (hereafter CS-BP model), was applied to forecast daily water quality of the Middle Route of South-to-North Water Diversion Project of China. Nine water quality indicators, including conductivity, chlorophyll content, dissolved oxygen, dissolved organic matter, pH, permanganate index, turbidity, total nitrogen, and water temperature were the predictand. Seven external environmental factors, including air temperature, five particulate matter (PM2.5), rainfall, sunshine duration, water flow, wind velocity, and water vapor pressure were the default predictors. A data pre-processing method was applied to select pertinent predictors. The results show that the CS-BP model has the best forecast accuracy, with the Mean Absolute Percentage Errors (MAPE) of 0.004%–0.33%, and the lowest Root Mean Square Error (RMSE) of each water quality indicator in comparison with traditional Back Propagation (BP) model, General Regression Neural Network model and Particle Swarm Optimization-Back Propagation model under default data proportion, 150:38 (training data: testing data). When training data reduced from 150 to 140, and from 140 to 130, the CS-BP model still produced the best forecasts, with the MAPEs of 0.014%–0.057% and 0.004%–1.154%, respectively. The results show that the CS-BP model can be an effective tool in daily water quality forecast with limited observed data. The improvement of the Cuckoo Search algorithm such as calculation speed, the forecast errors reduction of the CS-BP model, and the large-scale impacts such as land management on different water quality indicators, will be the focus of future research.

The risk analysis of an irrigation water allocation strategy based on physical mechanisms is critically important in practice. Conventional risk analysis only considers the role of the channel system and ignores the factors related to on-farm ponds. This paper proposes a channel-pond joint water supply mode (CPJM) based on copula approaches. Two copulas, the Plackett copula and No.16 copula, are chosen and two types of analyses are carried out with the proposed mode: (1) a risk assessment of CPJM with joint probability and conditional probability; and (2) determination of the water supply strategy given the pond water supply frequency. With a case study of the second channel in the Zhanghe Irrigation District (ZID), Southern China, nine combinations of channel water supply frequency (CWSF) and pond water supply frequency (PWSF) are studied. The results reveal that the failure probabilities of the joint distribution and the conditional distribution of the CPJM are 0.02%–16.54% and 0.45%–33.08%, respectively, with corresponding return period of 42–5000 and 10–222 years. Nevertheless, a previous study has shown that the real probability is 33.3%, which means that the return period is equals to three years. Therefore, the objective failure evaluation of the irrigation water-use strategy is useful for water saving in this channel system. Moreover, the irrigation water allocation strategy can be determined and the failure charts relating the CWSF and PWSF can be obtained for a predetermined PWSF. Thus, the channel-pond joint water supply mode provides a more reasonable estimate of the irrigation water allocation strategy reliability.