HIGHLIGHTS. A deep analysis based on last decade’ regionalization studies over the world.The parsimonious hydrological model seems more robust with limited prio

Reliable drought prediction plays a significant role in drought management. Applying machine learning models in drought prediction is getting popular in recent years, but applying the stand-alone models to capture the feature information is not sufficient enough, even though the general performance is acceptable. Therefore, the scholars tried the signal decomposition algorithm as a data pre-processing tool, and coupled it with the stand-alone model to build ‘decomposition-prediction’ model to improve the performance. Considering the limitations of using the single decomposition algorithm, an ‘integration-prediction’ model construction method is proposed in this study, which deeply combines the results of multiple decomposition algorithms. The model tested three meteorological stations in Guanzhong, Shaanxi Province, China, where the short-term meteorological drought is predicted from 1960 to 2019. The meteorological drought index selects the Standardized Precipitation Index on a 12-month time scale (SPI-12). Compared with stand-alone models and ‘decomposition-prediction’ models, the ‘integration-prediction’ models present higher prediction accuracy, smaller prediction error and better stability in the results. This new ‘integration-prediction’ model provides attractive value for drought risk management in arid regions.

Low flow simulation by hydrological models is a common solution in water research and application. However, knowledge about the influence of the objective functions is limited in relatively arid regions. This study aims to increase insight into the difference between the calibrated objective functions by evaluating eight objectives in three different classes (single objectives: KGE(log(Q)) and KGE(1/Q); multi objectives: KGE(Q)+KGE(log(Q)), KGE(Q)+KGE(1/Q), KGE(Qsort)+KGE(log(Qsort)) and KGE(Qsort)+KGE(1/Qsort); Split objectives: split KGE(Q) and split (KGE(Q)+KGE(1/Q))) in Bahe, a semi-arid basin in China. The calibrated model is Xin An Jiang, and the evaluation is repeated under varied climates. The results show a clear difference between objective functions for low flows, and the mean of KGE and logarithmic transformed-based KGE in time series (KGE(Q)+KGE(log(Q))) presents the best compromise between the estimation for low flows and general simulation. In addition, the applications of the inverse transformed-based KGE (KGE(1/Q)) and the Flow Duration Curve-based series (Qsort) in objectives are not suggested.

Flicker, defined as unnatural temporal fluctuations in perceived image intensity, is a common artifact in old films. Flicker removal is needed due to the high quality requirement of revitalizing old films. In this paper, we propose a least square estimation (LSE) method for eliminating flicker in film archives. The essential point of this method is to estimate flicker parameters for each small region by minimizing the squared error between corrected intensities in previous frames and estimated intensities in current frame. Based on the thresholds of flicker parameters, stationary and motion blocks are detected. For those stationary blocks, a criterion of mean squared error (MSE) is added to strongly restrict the stationary area. These blocks, in which MSEs surpass the threshold, are flagged as motion blocks. Flicker parameters in motion blocks are retrieved by iterative interpolation process. Synthetic and real flicker image sequences are used to evaluate and demonstrate the algorithm's usefulness in terms of average PSNR and visual quality in real-time playback respectively. Moreover, the results gotten from LSE method were compared with those obtained from Roosmalen method. The results of LSE method show an impressive improvement on PSNR in simulated flicker sequence. Meanwhile, no blocky effect and no new artifacts introduced are visible in real-time play back for both synthetic and real sequence.