Accurate forecasting of annual runoff time series is of great significance for water resources planning and management. However, considering that the number of forecasting factors is numerous, a single forecasting model has certain limitations and a runoff time series consists of complex nonlinear and nonstationary characteristics, which make the runoff forecasting difficult. Aimed at improving the prediction accuracy of annual runoff time series, the principal components analysis (PCA) method is adopted to reduce the complexity of forecasting factors, and a modified coupling forecasting model based on multiple linear regression (MLR), back propagation neural network (BPNN), Elman neural network (ENN), and particle swarm optimization-support vector machine for regression (PSO-SVR) is proposed and applied in the Dongbei Hydrological Station in the Ganjiang River Basin. Firstly, from two conventional factors (i.e., rainfall, runoff) and 130 atmospheric circulation indexes (i.e., 88 atmospheric circulation indexes, 26 sea temperature indexes, 16 other indexes), principal components generated by linear mapping are screened as forecasting factors. Then, based on above forecasting factors, four forecasting models including MLR, BPNN, ENN, and PSO-SVR are developed to predict annual runoff time series. Subsequently, a coupling model composed of BPNN, ENN, and PSO-SVR is constructed by means of a multi-model information fusion taking three hydrological years (i.e., wet year, normal year, dry year) into consideration. Finally, according to residual error correction, a modified coupling forecasting model is introduced so as to further improve the accuracy of the predicted annual runoff time series in the verification period.

Leopoldamys edwardsi is a species with wide distribution ranges in southern China but is not discussed in studies on geographic variation and species differentiation. We used 2 mitochondrial (Cytb, CO1) and 3 nuclear (GHR, IRBP and RAG1) genes to clarify species phylogeography and geographical differentiation. Maximum likelihood (ML) and Bayesian phylogenetic inference (BI) trees consistently indicated that L. edwardsi is a species complex containing 3 main lineages with high Kimura‐2‐parameter (K2P) divergences (i.e. lineages LN, LS and LHN) found in the northern and southern China and Hainan Island, respectively. The 3 species delimitation methods, automated barcoding gap discovery, Bayesian poisson tree process analysis and Bayesian phylogenetics and phylogeography, consistently supported the existence of cryptic species. Divergence times among the main lineages were inferred to be during the Pleistocene, with LHN/LS split at 1.33 Ma and LN/(LHN+LS) at 2.61 Ma; the diversifications of L. edwardsi complex might be caused by the rapid uplifts of Tibetan Plateau, paleoclimate change and complex topography. The divergence between LHN and LS was probably related to the separation of Hainan Island from the mainland via the formation of the Qiongzhou Strait. Lineages LN and (LS+LHN) likely diverged due to the Wuyi‐Nanling mountain range forming a dispersal barrier. Our results suggested that L. edwardsi complex contains at least 3 distinct species: LHN represents L. hainanensis, endemic to Hainan Island and previously considered as a subspecies L. e. hainanensis; LS represents a cryptic species distributed throughout the southern Chinese continent; and LN represents the nominotypical species L. edwardsi.

The complete sequence of the mitochondrial genome of the mandarin vole (Lasiopodomys mandarinus) was completed and annotated in this study. The circular genome is 16,375 bp in length and contains the typical 37 genes that are arranged in the same order as that of the putative ancestor of vertebrate, 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes and 2 non-coding regions. This study will provide genetic resource to clarify the taxonomic position of genus Lasiopodomys.

In this paper, automatic control of the water level in an irrigation canal by automatic regulation of intermediate gates was studied. Previous scholars have proposed a water level difference control strategy that works to keep relative deviations in all pools the same for a particular situation where the operator does not have full control over the canal inflow, with the centralized linear quadratic regulator (LQR) control method used. While in practice, the deviation tolerance of pools may differ in some canals which limits the applicability of the control strategy. In this work, a weight coefficient was added to the deviation and the algorithm was improved to keep the relative deviations to certain proportions. The model predictive control (MPC) method was then used with this improved control strategy and was compared to the LQR control method using the same control strategy. The results showed that the improved strategy can keep the water level deviations in all pools to certain proportions, as is our objective. Also, under this difference control strategy, the MPC method greatly improved the control performance compared to the LQR control method.

Murina huttoni rubella is a common Murina species in China, with a medium forearm length and reddish brown hairs. In this study, based on a male M. h. rubella individual from Jiangxi, China, its complete mitochondrial genome was sequenced and analyzed. The genome is 16,707 bp in length, including 22 tRNA genes, two rRNA genes, 13 protein-coding genes and a control region. The composition and arrangement of genes are similar to other bats. Phylogenetic trees that covered all released complete mitochondrial genome of bats were constructed using Bayesian Inference and maximum likelihood methods. Both phylogenetic results showed that M. h. rubella and M. ussuriensis have closer phylogenetic relationship. The complete mtDNA genome sequence of M. h. rubella would provide valuable information for solving taxonomic and phylogenetic problem in future.