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.

Real-time forecasting of the rainfall-runoff process is an effective means to reduce flood risk (Annis & Nadri 2019;Bates et al., 2020;Hartnett & Nash, 2017;Wang et al., 2017). Accurate rainfall information plays an essential role in this process since it is the primary driver for hydrological models during flood events (Li et al., 2016). Traditionally, ground-based rainfall observations have been widely used for hydrological forecasting due to their temporal resolution. However, their spatial coverage is typically not satisfactory, especially in remote regions, which translates into high forecast uncertainty. Recently, quasi-global satellite-based precipitation products (SPPs) have made it possible to develop spatial maps of rainfall with spatial resolutions that are finer than 0.25° and temporal resolutions that are shorter than a day, thereby providing a new opportunity for flood forecasting (

Five severe floods occurred in the Yangtze River Basin, China, between July and August 2020, and the Three Gorges Reservoir (TGR) located in the middle Yangtze River experienced the highest inflow since construction. The world’s largest cascade-reservoir group, which counts for 22 cascade reservoirs in the upper Yangtze River, cooperated in real time to control floods. The cooperation prevented evacuation of 600,000 people and extensive inundations of farmlands and aquacultural areas. In addition, no water spillage occurred during the flood control period, resulting in a world-record annual output of the TGR hydropower station. This work describes decision making challenges in the cooperation of super large reservoir groups based on a case-study, controlling the 4th and 5th floods (from Aug-14 to Aug-22), the efforts of technicians, multi-departments, and the state, and reflects on these. To realize the full potential of reservoir operation for the Yangtze River Basin and other basins with large reservoir groups globally, we suggest: (i) improve flood forecast accuracy with a long leading time; (ii) strengthen and further develop ongoing research on reservoir group cooperation; and (iii) improve and implement institutional mechanisms for coordinated operation of large reservoir groups.

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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.