The self‐calibrating Palmer drought severity index (scPDSI) was computed based on the four atmospheric reanalysis data sets combined with observational data over China during 1980–2015. The discrepancies of scPDSI among the four reanalysis data sets show the necessity of integrating multiple data sets. Drought characteristics, such as drought area, severity, duration, and frequency were examined based on multi‐data set mean scPDSI. The results reveal that significant drying trends are found in Qinghai‐Tibet Plateau, southwest, southeast and entire China. Drought area (drought severity) has increased (decreased) by about 1.16% (0.015%) per decade over entire China. Trends in drought duration, temporally averaged severity and frequency also indicate that droughts become more serious in each region during the past 36 years. The identification of drought events in each month by the clustering algorithm shows that droughts over China are more and more frequent. In addition, 65 separate drought events with the duration longer than 3 months were identified under the area threshold of 150,000 km². Through severity‐area‐duration analysis, the 2005–2015 drought is found to be the prominent event.

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.

Medium-term hydrological streamflow forecasting can guide water dispatching departments to arrange the discharge and output plan of hydropower stations in advance, which is of great significance for improving the utilization of hydropower energy and has been a research hotspot in the field of hydrology. However, the distribution of water resources is uneven in time and space. It is important to predict streamflow in advance for the rational use of water resources. In this study, a Bayesian model average integrated prediction method is proposed, which combines artificial intelligence algorithms, including long-and short-term memory neural network (LSTM), gate recurrent unit neural network (GRU), recurrent neural network (RNN), back propagation (BP) neural network, multiple linear regression (MLR), random forest regression (RFR), AdaBoost regression (ABR) and support vector regression (SVR). In particular, the simulated annealing (SA) algorithm is used to optimize the hyperparameters of the model. The practical application of the proposed model in the ten-day scale inflow prediction of the Three Gorges Reservoir shows that the proposed model has good prediction performance; the Nash–Sutcliffe efficiency NSE is 0.876, and the correlation coefficient r is 0.936, which proves the accuracy of the model.

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.

The complexity of agricultural water management practices has challenged researchers attempting to determine how hydrological pathways affect water and nitrogen (N) losses. Field experiments in a typical rice agricultural catchment in the Zhanghe Irrigation District in China were conducted in 2014 and 2015 to evaluate and quantify the connections between hydrological pathways and water and N losses. Four water management periods—namely, the irrigation period (IP), the rainfall period (RP), the normal-drainage period (DP), and the nonirrigation-drainage period (NP)—were defined based on climate conditions and artificial irrigation and drainage periods. The experimental results indicated that most water and N losses occurred in DP and RP and that lateral seepage and tailwater were the main sources of the observed drainage. Low average concentrations of ammonia N (${\mathrm{NH}}_4^{+}─\mathrm{N}$ , $0.373\mathrm{mg}{\mathrm{L}}^{-1}$ ) and nitrate N (${\mathrm{NO}}_3^{-}─\mathrm{N}$ , $0.285\mathrm{mg}{\mathrm{L}}^{-1}$ ) and low proportions of dissolved N (42.2–54.0%) were detected in association with green manure application, whereas the average concentration of total N (TN, $1.376\mathrm{mg}{\mathrm{L}}^{-1}$ ) was relatively high, which resulted in slight N pollution in the studied agricultural area. The concentrations and proportions of dissolved N were greastest in DP and RP. The average daily mass fluxes of ${\mathrm{NH}}_4^{+}─\mathrm{N}$ , ${\mathrm{NO}}_3^{-}─\mathrm{N}$ , and TN were 0.027, 0.020, and $0.095\mathrm{kg}{\mathrm{ha}}^{-1}{\mathrm{day}}^{-1}$ , respectively, and the magnitudes in DP and RP were significantly greater than those in IP and NP. The average concentrations of ${\mathrm{NH}}_4^{+}─\mathrm{N}$ , ${\mathrm{NO}}_3^{-}─\mathrm{N}$ , and TN were not correlated to drainage discharge according to a Pearson correlation analysis, whereas a power function can quantify the relationship between N mass fluxes and discharge during various water management periods. The average thresholds of drainage discharge, at which N export from paddy fields equalled to zero, were $45.2{\mathrm{m}}^3{\mathrm{day}}^{-1}{\mathrm{ha}}^{-1}$ for ${\mathrm{NH}}_4^{+}─\mathrm{N}$ , $51.9{\mathrm{m}}^3{\mathrm{day}}^{-1}{\mathrm{ha}}^{-1}$ for ${\mathrm{NO}}_3^{-}─\mathrm{N}$ , and $55.5{\mathrm{m}}^3{\mathrm{day}}^{-1}{\mathrm{ha}}^{-1}$ for TN. These findings indicate that controlling drainage discharge is important for the reduction of water loss and N loss from paddy fields.