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This study evaluated and intercompared seven near-real-time (NRT) versions of satellite-based precipitation products (SPPs) with latencies of less than one day, including GSMaP-NRT, GSMaP-Gauge-NRT, GSMaP-NOW, IMERG-Early, IMERG-Late, TMPA 3B42RT, and PERSIANN-CCS for wet seasons from 2008 to 2019 in a typical middle–high latitude temperate monsoon climate basin, namely, the Nierji Basin in China, in four aspects: flood sub-seasons, rainfall intensities, precipitation events, and hydrological utility. Our evaluation shows that the cell-scale and area-scale intercomparison ranks of NRT SPPs are similar in these four aspects. The performances of SPPs at the areal scale, at the event scale, and with light magnitude are better than those at the cell scale, at the daily scale, and with heavy magnitude, respectively. Most SPPs are similar in terms of their Pearson Correlation Coefficient (CC). The main difference between SPPs is in terms of their root-mean-square error (RMSE). The worse performances of TMPA 3B42RT are mainly caused by the poor performances during main flood seasons. The worst performances of PERSIANN-CCS are primarily reflected by the lowest CC and the underestimation of precipitation. Though GSMaP-NOW has the highest RMSE and overestimates precipitation, it can reflect the precipitation variation, as indicated by the relatively high CC. The differences among SPPs are more significant in pre-flood seasons and less significant in post-flood seasons. These results can provide valuable guidelines for the selection, correction, and application of NRT SPPs and contribute to improved insight into NRT-SPP retrieval algorithms.

The study on developing the reasonable safety monitoring indexes plays a most importantly role in the health monitoring of high core rockfill dams. However, researches on this topic are relatively scarce both at home and abroad. In this paper, the characteristics and failure modes of seepage in high core rockfill dam are analyzed firstly. Then, a safety monitoring index based on seepage quantity, which reflects the overall seepage behavior, is developed, using the real-time monitoring data and its safety monitoring model. Moreover, another safety monitoring index based on seepage gradient, reflecting the local seepage behavior, is proposed, combining the spatial layout of osmo- meters and local failure mechanisms of core wall. Additionally, one more safety monitoring index based on permeability coefficient, which considers the overall and local seepage behaviors, is developed, on the basis of establishing the finite element analysis model and real-time seepage coefficient inversion analysis model of high core rockfill dam. A case study on these indexes of Nuozhadu high core rockfill dam is developed, which improves the reliability of seepage safety evaluation of the dam.

The modeling of cracks and identification of dam behavior changes are difficult issues in dam health monitoring research. In this paper, a time-varying identification model for crack monitoring data is built using support vector regression (SVR) and the Bayesian evidence framework (BEF). First, the SVR method is adopted for better modeling of the nonlinear relationship between the crack opening displacement (COD) and its influencing factors. Second, the BEF approach is applied to determine the optimal SVR modeling parameters, including the penalty coefficient, the loss coefficient, and the width coefficient of the radial kernel function, under the principle that the prediction errors between the monitored and the model forecasted values are as small as possible. Then, considering the predicted COD, the historical maximum COD, and the time-dependent component, forewarning criteria are proposed for identifying the time-varying behavior of cracks and the degree of abnormality of dam health. Finally, an example of modeling and forewarning analysis is presented using two monitoring subsequences from a real structural crack in the Chencun concrete arch-gravity dam. The findings indicate that the proposed time-varying model can provide predicted results that are more accurately nonlinearity fitted and is suitable for use in evaluating the behavior of cracks in dams.

The influences of gauge block gestures for calibrating coordinate measuring machine(CMM) attach importance to people more and more. In particularly, gauge block gestures for metrology CMM can not be ignored to which the accuracy of linearity measurement is hyper to 0.6μm + 1000/L. Therefore, adjustable platform is developed and single-frequency laser interferometer is adopted as measuring datum to measure the influences of length variation of gauge blocks in different gestures. At the same time, advance multi-point bracing means to reduce the length variation of gauge blocks. The results of experiments indicate that multi-point bracing means make the maximum length variation of gauge blocks to be +0.07/μm. According to the uncertainty formula of indicating error to CMM, the standard uncertainty is recalculated as u″ c = +0.73μm.