This study applied a GR4J model in the Xiangjiang and Qujiang River basins for rainfall-runoff simulation. Four recurrent neural networks (RNNs)—the Elman recurrent neural network (ERNN), echo state network (ESN), nonlinear autoregressive exogenous inputs neural network (NARX), and long short-term memory (LSTM) network—were applied in predicting discharges. The performances of models were compared and assessed, and the best two RNNs were selected and integrated with the lumped hydrological model GR4J to forecast the discharges; meanwhile, uncertainties of the simulated discharges were estimated. The generalized likelihood uncertainty estimation method was applied to quantify the uncertainties. The results show that the LSTM and NARX better captured the time-series dynamics than the other RNNs. The hybrid models improved the prediction of high, median, and low flows, particularly in reducing the bias of underestimation of high flows in the Xiangjiang River basin. The hybrid models reduced the uncertainty intervals by more than 50% for median and low flows, and increased the cover ratios for observations. The integration of a hydrological model with a recurrent neural network considering long-term dependencies is recommended in discharge forecasting.

Agricultural drought can have long-lasting and harmful impacts on both the ecosystem and economy. Therefore, it is important to monitor and predict agricultural drought accurately. Soil moisture is the key variable to define the agricultural drought index. However, in situ soil moisture observations are inaccessible in many areas of the world. Remote sensing techniques enrich the surface soil moisture observations at different tempo-spatial resolutions. In this study, the Level 2 L-band radiometer soil moisture dataset was used to estimate the Soil Water Deficit Index (SWDI). The Soil Moisture Active Passive (SMAP) dataset was evaluated with the soil moisture dataset obtained from the China Land Soil Moisture Data Assimilation System (CLSMDAS). The SMAP-derived SWDI (SMAP_SWDI) was compared with the atmospheric water deficit (AWD) calculated with precipitation and evapotranspiration from meteorological stations. Drought monitoring and comparison were accomplished at a weekly scale for the growing season (April to November) from 2015 to 2017. The results were as follows: (1) in terms of Pearson correlation coefficients (R-value) between SMAP and CLSMDAS, around 70% performed well and only 10% performed poorly at the grid scale, and the R-value was 0.62 for the whole basin; (2) severe droughts mainly occurred from mid-June to the end of September from 2015 to 2017; (3) severe droughts were detected in the southern and northeastern Xiang River Basin in mid-May of 2015, and in the northern basin in early August of 2016 and end of November 2017; (4) the values of percentage of drought weeks gradually decreased from 2015 to 2017, and increased from the northeast to the southwest of the basin in 2015 and 2016; and (5) the average value of R and probability of detection between SMAP_SWDI and AWD were 0.6 and 0.79, respectively. These results show SMAP has acceptable accuracy and good performance for drought monitoring in the Xiang River Basin.

Stabilizing specific intermediates to produce CH₄ remains a main challenge in solar‐driven CO₂ reduction. Herein, g‐C₃N₄ is modified with saturated and lacunary phosphotungstates (PW_x, x=12, 11, 9) to tailor the CO₂ reduction pathway to yield CH₄ in high selectivity. Increased lacuna of phosphotungstates leads to higher CH₄ yield and selectivity, with a superior CH₄ selectivity of 80 % and 40.8 μmol ⋅ g⁻¹ ⋅ h⁻¹ evolution rate for PW₉/g‐C₃N₄. Conversely, g‐C₃N₄ and PW_x alone show negligible CH₄ production. The conversion of CO₂ to CH₄ follows a tandem catalytic process. CO₂ is initially activated on g‐C₃N₄ to form *CO intermediates, meanwhile photogenerated electrons derived from g‐C₃N₄ transfer to PW_x. Then the reduced PW_x captures *CO, which is subsquently hydrogenated to CH₄. With the injection of two photogenerated electrons, PW₉ is capable of adsorbing and activating *CO. However, the reduced PW₁₂ and PW₁₁ are incapable of adsorbing *CO due to the small energy of occupied molecular orbitals, which is the reason for the poorer activity of PW_x/g‐C₃N₄ (x=12, 11) compared with that of PW₉/g‐C₃N₄. This work provides new insights to regulate highly selective CO₂ photoreduction to CH₄ by utilizing lacuna of polyoxometalates to enhance the interaction of metals in polyoxometalates with key intermediates.