Canal lining is commonly used to reduce seepage loss and increase water use efficiency. However, few studies have quantitatively estimated the seepage control effects of different lining materials under different service times. Ponding tests were conducted on the same canal section with four different lining statuses to investigate the canal lining effect on seepage control and its impact factors in arid areas. The cracks and holes in different lining materials were surveyed, and the canal seepage rates under the four test treatments were calculated by monitoring the water level change in the canal. The results show that the cracks in the joints of the two precast concrete slabs and holes in the geomembrane, which are located 0.25 m above the canal bottom on two sides of the canal, are responsible for the increased seepage loss. The new concrete and geomembrane lining combination reduces seepage by 86% compared with no lining, while seepage can be reduced by 68% using the concrete and geomembrane lining combination after three service years, and the amount decreases to 11% by using geomembrane lining with a three year service time. Based on the experiment and literature, a statistical relationship between the seepage reduction and lining service time was established, which provided a possible and easy way to estimate seepage losses from lined canals and improve the estimation accuracy using an empirical formula. Without considering the service time lining effect, the seepage loss is underestimated by 58%, and the canal water use efficiency is overestimated.

Cotton suffers from alternations of flood and drought in China. A lysimeter trial was conducted to investigate the responses of various cotton yield indices under water-stress treatments including, flood (five-day, eight-day), drought (10-day, 15-day), and five-day flood followed by 10-day drought, during the flowering and boll-forming stage. The results showed that the seed cotton yield was significantly (p < 0.05) reduced under all water-stress treatments, while the harvest index was not affected under any treatment. Significant decreases in dry matter yield, boll number, and boll hull mass were detected under flood treatments but not under drought treatments. The percentage cotton yield losses per day induced by flood and drought were 6.22% and 2.48%, respectively. Under water stress, the associations between seed cotton yield and relevant yield indices were weakened, but yield losses were still strongly related to the decreases in dry matter yield and boll number. Flood followed by drought caused significant reductions in all yield indices except harvest index; however, the reduction was much lower than the additive reductions induced by flood and drought. These results provide bases for scheduling irrigation and drainage under climate change.

Waterlogging crop disasters are caused by continuous and excessive soil water in the upper layer of soil. In order to enable waterlogging monitoring, it is important to collect continuous and accurate soil moisture data. The distributed hydrology soil vegetation model (DHSVM) is selected as the basic hydrological model for soil moisture estimation and winter-wheat waterlogging monitoring. To handle the error accumulation of the DHSVM and the poor continuity of remote sensing (RS) inversion data, an agro-hydrological model that assimilates RS inversion data into the DHSVM is used for winter-wheat waterlogging monitoring. The soil moisture content maps retrieved from satellite images are assimilated into the DHSVM by the successive correction method. Moreover, in order to reduce the modeling error accumulation, monthly and real-time RS inversion maps that truly reflect local soil moisture distributions are regularly assimilated into the agro-hydrological modeling process each month. The results show that the root mean square errors (RMSEs) of the simulated soil moisture value at two in situ experiment points were 0.02077 and 0.02383, respectively, which were 9.96% and 12.02% of the measured value. From the accurate and continuous soil moisture results based on the agro-hydrological assimilation model, the waterlogging-damaged ratio and grade distribution information for winter-wheat waterlogging were extracted. The results indicate that there were almost no high-damaged-ratio and severe waterlogging damage areas in Lixin County, which was consistent with the local field investigation.

Accurate estimation of crop yield under aeration stress is crucial for field water table management. In this study, the CROPR crop model was improved in two aspects: (i) a new aeration factor, which was related to a drainage index, was proposed and used to represent the condition of soil aeration; and (ii) a multiplicative structure, instead of the original additive structure, was used in the calculation of dry matter accumulation to include the after-effect of aeration stress. Four-year lysimeter experiments on cotton (Gossypium hirsutum L.) growth under aeration stress were conducted from 2008 to 2011 to calibrate and validate both the original and improved CROPR. The results indicated that the improved CROPR performed better than the original CROPR and was suitable for simulating cotton yield under aeration stress. In the calibration, with the improved CROPR, the root-mean-squared error (RMSE) of seed cotton yield was 832.84 kg ha–1 with a normalised value (NRMSE) of 15.87%, whereas with the original CROPR, the RMSE was 973.03 kg ha–1 with an NRMSE of 18.55%. In the validation, with the improved CROPR, the RMSE of seed cotton yield was 686.22 kg ha–1 with an NRMSE of 14.87%; with the original CROPR, the RMSE was 1019.02 kg ha–1 with an NRMSE of 22.08%.