Real-time flood forecasting updating is essential in improving the forecasting performance and preventing flood damages. The advanced dynamic system response curve (DSRC) method has been validated to be effective by adjusting precipitation based on simulated streamflow errors. However, the time lag between input-output signals is not explicitly considered in the original DSRC, resulting in the problem that the most recent precipitation information is not utilized in updating the forecasting. Moreover, regularization techniques are normally introduced in DSRC to ensure the numerical stability of error estimation, however, the commonly used Ridge estimator can result in excessive adjustment of precipitation. To address these critical issues, we proposed an improved precipitation adjustment framework (DSRC-ARMA) that integrated the DSRC method and the autoregressive-moving average (ARMA) model, such that the most recent precipitation information can be used for a complete precipitation adjustment. Moreover, alternative regularized estimators (i.e., the Lasso and Elastic Net estimators) were introduced and cross-compared to prevent the excessive adjustment issue. The performance of the proposed framework was evaluated in two basins in China. The results showed that the DSRC-ARMA method outperformed the original DSRC method in terms of overall goodness-of-fit (e.g., Nash-Sutcliffe efficiency improved from 0.94 f 0.03 to 0.95 f 0.04 and 0.89 f 0.05 to 0.91 f 0.05, respectively in Dapoling (DPL) and Jianyang (JY) basin) and particularly capturing the peak flows (relative error of peak flow decreased from 13.6 f 7.3 % to 5.2 f 3.7 % and from 10.1 f 7.8 % to 5.9 f 3.5 % in DPL and JY, respectively). For different regularized estimators, the Ridge estimator was most suitable for the rainfall events without intermittent non-rainfall time segments (due to its veracity feature); while the Lasso estimator performed better for intermittent rainfall events, due to its feature of sparsity that can confine non-rainfall period errors to be zeros and thus avoid excessive adjustment. Overall, the proposed precipitation adjustment framework holds the potential to enhance the real-time flood forecasting accuracy, thereby offering a valuable approach for flood mitigation.

Lunar-based equipment undertakes the task of movement and transportation in the construction of unmanned lunar base. In the process of moving, the mechanical legs of the equipment are influenced by the lunar soil with special mechanical properties. In order to avoid these uncertainties caused by the lunar soil and other lunar environmental conditions affecting the safety during the mission, a new robust control method with the form of sectionalized expression is proposed based on the dynamic model of the leg-soil system. Lyapunov second method is introduced to demonstrate that the proposed control method can maintain the stability of the leg-soil system successfully. In order to clarify the contact force in the dynamics model, CAS-1 lunar soil simulant that can accurately simulate real lunar soil is used in the calibration test to obtain the precise mechanical parameters. Simulation and experiment are also carried out to verify the proposed control method and the traditional control methods are introduced to make a comparison. Both the simulation and experiment results show that the proposed control method has a better control effect than traditional methods. The proposed method improves the accuracy by an average of 75.7% and 55.9% compared to the traditional methods and the error is limited to 0.2%. By maintaining the stability and accuracy of the leg-soil system, the stability of the lunar-based equipment is improved when performing construction tasks. This study lays the foundation for the construction of unmanned lunar base in advance.

Despite that the supplying role of cryosphere (glaciers, permafrost, and snow) in groundwater storage (GWS) in Tibetan Plateau (TP) is well-known by comparing their long-term linear trends, the question whether GWS could in turn affect the variation of cryospheric variables remains controversial, since long-term trend analysis fails to distinguish the direction of their interplay. To find evidence of GWS causally affecting cryosphere, this research resorts to the causal inference community and investigates a novel causal interaction between GWS and cryosphere in TP: nonlinear dynamic causality (NDC), based on the Nonlinear Dynamic System (NDS) theory. The specific method applied is called Convergent Cross-mapping (CCM), which detects NDC between two targeted variables X and Y from both directions (X & RARR; Y, Y & RARR; X). Important findings are summarized as follows: (1) With CCM, NDCs with similar strengths are found from glaciers retreat, snowmelt, and permafrost thaw to GWS, respectively; (2) Also in the form of NDC, GWS is proven to reversely affect permafrost, but not to glacier and snow; (3) NDCs are also found between GWS and other hydrometeorological variables in TP, including lakes, soil moisture, precipitation, and temperature; (4) Some nontraditional NDCs from glaciers and lakes towards GWS are identified. Overall, using CCM, our new findings about NDC answer the controversial question of whether GWS could in turn affect cryosphere, completing previous conclusions about how GWS interplays with cryosphere in TP, and more importantly, this research would shed light on future causality detection in hydrology.