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Precipitation plays a critical role in hydrometeorological studies. A predictive analysis of gridded rainfall datasets may provide a cost-effective alternative to conventional rain gauge observations. Here, our objective is to evaluate the performance of satellite and reanalysis precipitation products in the hydrological modeling of a mesoscale watershed. The research also examines the accuracy of hydrological simulations in a sizeable flood-prone watershed in the absence of observed data associated with the myriad water retaining structures present in the catchment. We use three precipitation products, namely Tropical Rainfall Measurement Missions (TRMM) 3B42 Version 7, Climate Forecast System Reanalysis (CFSR), and daily precipitation data recorded at multiple rain gauges in the upper Huai River Basin to simulate streamflow. The Soil & Water Assessment Tool (SWAT) is utilized for runoff modeling, while SWAT-CUP is used to perform sensitivity analysis and to calibrate and validate the simulation results. Nash–Sutcliffe efficiency, percent bias, and Kling-Gupta efficiency (KGE) are employed to evaluate modeling efficiency for three precipitation datasets on different temporal scales. The results indicate that TRMM and CFSR datasets provide satisfactory results on both daily and monthly scales. Specifically, the SWAT model performs better at monthly simulations than daily simulations for all precipitation datasets used. HIGHLIGHTS Examining the applicability of TRMM and CFSR daily precipitation products in simulating runoff from a large watershed.; Calibrating a hydrological model in the absence of data associated with extensive anthropogenic activities for flood prevention and diversion.; Performing streamflow simulations using a SWAT model and parameter calibration and sensitivity analysis using a SUFI-2 algorithm.

Seawater intrusion in the Minjiang River estuary has gravely endangered the water security of the surrounding area in recent years. Previous studies mainly focused on exploring the mechanism of intrusion, but failed to provide a scheme for suppressing seawater intrusion. The three most relevant determinants to chlorine level, which represented the strength of seawater intrusion, were determined using Pearson correlation analysis as being the daily average discharge, daily maximum tidal range, and daily minimum tidal level. Considering the lower requirement of sample data and the ability to handle high-dimensional data, the random forest algorithm was used to construct a seawater intrusion suppression model and was combined with a genetic algorithm. The critical river discharge for suppressing estuary seawater intrusion determined using this model. The critical river discharge was found to gradually increase with the maximum tidal range, which in three different tide scenarios was 487 m3/s, 493 m3/s, and 531 m3/s. The practicable seawater intrusion suppression scheme was built up with three phases to make it easier to regulate upstream reservoirs. In the scheme, the initial reading of river discharge was 490 m3/s, and it rose to 650 m3/s over six days, from four days before the high tide’s arrival to two days following it, and before falling down to 490 m3/s at the end. Verified with the 16 seawater intrusion events in the five dry years, this scheme could eliminate 75% of the seawater intrusion risk and effectively reduce the chlorine level for the remaining 25% of events.

Background LncRNAs take various effects mostly through sponging with miRNAs. Based on public databases and integrating bioinformatics analyses and further validated in BC tissue and cell lines, the effect of lncRNA AFAP1-AS1 on breast cancer cells proliferation and migration was verified and might work via miR-21/PTEN axis. Methods The expression of AFAP1-AS1, miR-21, PTEN in BC tissues and cell lines were analyzed by qRT-PCR. BC cell proliferation and migration were examined using EdU, colony formation assays and wound healing, transwell assays, respectively. The cellular localization of AFAP1-AS1 was analyzed using FISH. RNA pull down assay was performed to analyze the binding of AFAP1-AS1 to miR-21. The interaction between miR-21 and PTEN was examined using a dual luciferase reporter assay. The effect of AFAP1-AS1 on BC tumor growth in vivo was analyzed using a tumor xenograft mouse model. Results The expression of AFAP1-AS1, which was significantly upregulated in BC tissues and cell lines. The higher expression of AFAP1-AS1 was correlated with old age and lymph node metastasis of patients with BC. AFAP1-AS1 knockdown BC cells exhibited decreased proliferation and migration in vitro and in vivo. Additionally, AFAP1-AS1 knockdown downregulated miR-21 expression and upregulated PTEN expression. Mechanistically, the interaction between AFAP1-AS1 and miR-21 promotes BC cell proliferation and migration. AFAP1-AS1 knockdown decreased BC cell proliferation and migration, which are regulated by PTEN, through miR-21 regulation. Conclusions The knockdown of lncRNA AFAP1-AS1 upregulated PTEN expression and consequently attenuated miR-21-mediated enhanced BC cell proliferation and migration. LncRNA AFAP1-AS1 is a potential prognostic biomarker for BC patients.

Streamflow plays a major role in the optimal management and allocation of available water resources in each region. Reliable techniques are therefore needed to be developed for streamflow modeling. In the present study, the performance of streamflow modeling is improved via developing novel boosted models. The daily streamflows of four hydrometric stations comprising of the Brantford and Galt stations located on the Grand River, Canada, as well as Macon and Elkton stations respectively, located on the Ocmulgee and Umpqua rivers, United States, are used. Three different types of boosted models are implemented and proposed by coupling the classical multi-layer perceptron (MLP) with the optimization algorithms, including particle swarm optimization (PSO) and coupled particle swarm optimization-multi-verse optimizer (PSOMVO) and a time series model, namely the bi-linear (BL). So, the boosted MLP-PSO, MLP-PSOMVO, and MLP-BL models are developed. The accuracy of all the boosted models is compared with the classical MLP and BL by the statistical metrics used. It is concluded that all the boosted models developed at the studied stations lead to superior modeling results of the daily streamflows to the classical MLP; however, the boosted MLP-BL models generally outperformed the MLP-PSO and MLP-PSOMVO ones.

Evaporation is one of the vital components of hydrological cycle. Precise estimation of pan evaporation (Epan) is essential for the sustainable water resources management. The current study proposed a novel approach to estimate daily Epan across the humid region of Iran using support vector regression (SVR) technique coupled with Krill Herd Algorithm (SVR-KHA). Meteorological data were collected from three stations (Bandar Abbas, Rudsar, and Osku) over a period from 2008 to 2018 and used for application. Of the data, 70% were used for training and remaining 30% were used for testing. The study considered seven different combinations of input variables for predicting daily Epan at each station. The influence of KHA hybridization is examined by comparing results of SVR-KHA algorithm with simple SVR through a multitude of statistical performance evaluation criteria such as coefficient of determination (R2), Wilmot’s index (WI), root-mean-square error (RMSE), Mean Absolute Error (MAE), Relative Root Mean Square Error (RRMSE), Mean Absolute Relative Error (MARE), and several graphical tools. Single input SVR1 model hybrid with KHA (SVR-KHA1) showed improved performance (R2 of 0.717 and RMSE of 1.032 mm/day) as compared with multi-input SVR models, e.g., SVR5 (with RMSE and MAE of 1.037 mm/day and 0.773 mm/day), while SVR7 model hybridized with KHA (SVR-KHA7), which considers seven meteorological variables as input, performed best as compared with other models considered in this study. Epan estimates at Bandar Abbas and Rudsar by SVR and SVR-KHA are similar (with R2 statistics values of 0.82 and 0.84 at Bandar Abbas station, and 0.88 and 0.9 at Rudsar station, respectively). However, better improvements in Epan estimates are observed at Osku station (with R2 of 0.91 and 0.86, respectively), which is situated at interior geographical location with a higher altitude than the other two coastal stations. Overall, the results showed consistent performance of SVR-KHA model with stable residuals of lower magnitude as compared with standalone SVR models.

Accurate runoff forecasting plays a key role in catchment water management and water resources system planning. To improve the prediction accuracy, one needs to strive to develop a reliable and acc...

Field capacity (FC) and permanent wilting point (PWP) are two important properties of the soil when the soil moisture is concerned. Since the determination of these parameters is expensive and time-consuming, this study aims to develop and evaluate a new hybrid of artificial neural network model coupled with a whale optimization algorithm (ANN-WOA) as a meta-heuristic optimization tool in defining the FC and the PWP at the basin scale. The simulated results were also compared with other core optimization models of ANN and multilinear regression (MLR). For this aim, a set of 217 soil samples were taken from different regions located across the West and East Azerbaijan provinces in Iran, partially covering four important basins of Lake Urmia, Caspian Sea, Persian Gulf-Oman Sea, and Central-Basin of Iran. Taken samples included portion of clay, sand, and silt together with organic matter, which were used as independent variables to define the FC and the PWP. A 80–20 portion of the randomly selected independent and dependent variable sets were used in calibration and validation of the predefined models. The most accurate predictions for the FC and PWP at the selected stations were obtained by the hybrid ANN-WOA models, and evaluation criteria at the validation phases were obtained as 2.87%, 0.92, and 2.11% respectively for RMSE, R2, and RRMSE for the FC, and 1.78%, 0.92, and 10.02% respectively for RMSE, R2, and RRMSE for the PWP. It is concluded that the organic matter is the most important variable in prediction of FC and PWP, while the proposed ANN-WOA model is an efficient approach in defining the FC and the PWP at the basin scale.

Most chronic kidney diseases (CKDs) develop to end-stage renal disease (ESRD), which is characterized by fibrosis and permanent tissue and function loss. As a result, better and more effective remedies are essential. Kaempferol (KAE) is a common flavonoid extracted from plants. It can control the progression of kidney fibrosis and the epithelial-to-mesenchymal transition (EMT) of the renal tubular system.We aim to investigate the effect of KAE therapy on extracellular matrix deposition and stimulation of EMT in vitro and in vivo to elucidate the treatment mechanisms regulating these effects.Chronic hypertension-induced kidney fibrosis was studied in spontaneously hypertensive rats with chronic kidney disease. Biochemical analysis, histological staining, and the expression level of relative proteins were used to assess the effect of KAE on renal function and fibrosis. The direct impact of KAE on proliferation and migration was evaluated using human renal tubular epithelial cells (HK-2) induced by transforming growth factor-β1 (TGF-β1), which can then induce EMT. The molecular mechanism of KAE was verified using co-IP assay and immunofluorescence.KAE could reduce blood pressure and decrease the extracellular matrix (ECM) components (including collagen I and collagen Ш), TGF-β1, and α-SMA in the kidneys of hypertension-induced rats with chronic kidney disease. Moreover, in HK-2 cell treated with TGF-β1, KAE administration significantly suppressed proliferation, migration, and EMT via increasing the expression of E-cadherin, while reducing the N-cadherin and α-SMA. Sufu was exceedingly repressed in HK-2 cells treated with TGF-β1. KAE inhibited the activation of Shh and Gli through increasing the expression of Sufu, thereby blocking the nuclear translocation of Gli1 in vitro.KAE ameliorated kidney fibrosis and EMT by inhibiting the sonic hedgehog signaling pathway, thereby to attenuate the pathological progression of hypertensive kidney fibrosis.

Lakes have an important role in storing water for drinking, producing hydroelectric power, and environmental, agricultural, and industrial uses. In order to optimize the use of lakes, precise prediction of the lake water level (LWL) is a main issue in water resources management. Due to the existence of nonlinear relations, uncertainty, and characteristics of the time series variables, the exact prediction of the lake water level is difficult. In this study the hybrid support vector regression (SVR) and the grey wolf algorithm (GWO) are used to predict lake water level fluctuations. Also, three types of data preprocessing methods, namely Principal component analysis, Random forest, and Relief algorithm were used for finding the best input variables for prediction LWL by the SVR and SVR-GWO models. Before the LWL simulation on monthly time step using the hybrid model, an evolutionary approach based on different monthly lags was conducted for determining the best mask of the input variables. Results showed that based on the random forest method, the best scenario of the inputs was Xt&minus, 1,&nbsp, Xt&minus, 2,&nbsp, 3,&nbsp, 4 for the SVR-GWO model. Also, the performance of the SVR-GWO model indicated that it could simulate the LWL with acceptable accuracy (with RMSE = 0.08 m, MAE = 0.06 m, and R2 = 0.96).

Located in Batam City, Duriangkang Dam plays an important role in the area's clean water provision regarding the fact that it is the first and largest estuary dam in Indonesia. However, with its total effective volume of 107,000,000 m 3 , Duriangkang Dam is now heavily polluted due to the enormous wastewater, both domestic and industrial, that are directly discharged into the dam. Moreover, the practice of fish farming has also contributed in making it worse. From the water sampling taken from 2016 to 2018, the water condition in Duriangkang Dam is no longer classified as the 2 nd class of raw water standard in Indonesia. This study employs the WASP model to evaluate and simulate the possible solution to control the water pollution rate in Duriangkang Dam. The water quality indicators, namely dissolved Oxygen (DO), total phosphorus (TP), and biochemical oxygen demand (BOD) will be modeled in this study. The calibration and verification results indicate the model shows excellent fitting for DO and TP but slightly less for BOD. Under several scenarios, it is identified that the best way to control the pollution rate within the dam is by implementing scenario 8 that totally eliminates the fish farm and domestic wastewater load.

This paper presents the application of sluice gates to control the excess runoff in the wide and very mild channel in an industrial area which is located in the Cikande, Banten, Indonesia. St. Venant equation is used and solved by 1-D model HEC-RAS. The result shows that the main channel can be utilized as a long storage or long reservoir if it is combined with sluice gates. Various scenarios of gates opening and closing operation have been conducted in order to determine the most effective gates operation. Two options of gate operations have been analyzed. The first option is the automatic option in which the gates are able to open and close automatically at a certain water level in the main channel. The second is manual option where the gates constantly open at a certain height. The results show that the main channel successfully reduced the 10 years of flood discharge from 37.45 to 17.37 m3/s for the automatic gates and from 37.45 to 17.38 m3/s for the constant opening. Although both options give the same result for controlling the flood, there are several differences between the options, especially the capability of both the systems to provide raw water for the industrial purposes. In conclusion, the automatic operation is the most effective operation, despite the fact that it is more expensive and harder to implement than the manual operation.

River suspended sediment load (SSL) estimation is of importance in water resources engineering and hydrological modeling. In this study, a novel hybrid approach is recommended for SSL estimation in which multi-layer perceptron (MLP) is hybridized with particle swarm optimization (PSO) and then, integrated with differential evolution algorithm (DE) called as MLP-PSODE. The hybrid MLP-PSODE model is implemented to model the SSL of Mahabad river located at northwest of Iran. For the sake of examination of the MLP-PSODE model performance, several techniques including multi-layer perceptron (MLP), multi-layer perceptron integrated with particle swarm optimization (MLP-PSO), radial basis function (RBF) and support vector machine (SVM) are selected as benchmarks. For this purpose, five different scenarios are considered for the modeling. The results indicated that the new hybrid model of MLP-PSODE is successful in estimating SSL by considering single input of discharge (Q) with high accuracy as compared to its alternatives with RMSE = 1794.4 ton·day−1, MAPE = 41.50% and RRMSE = 107.09%, which were much lower than those of MLP based model with RMSE = 3133.7 ton·day−1, MAPE = 121.40% and RRMSE = 187.03%. The developed MLP-PSODE model, not only outperforms its counterparts in terms of accuracy in extreme values estimation, but also it is found as a parsimonious model that incorporates lower number of input parameters in its structure for SSL estimation.

A large amount of phytoplankton blooms have occurred in the Jiangsu coastal waters since 2000. The trigger of the phytoplankton blooms responds to environmental factors. The present study aims to investigate the phytoplankton bloom development from the physical properties. To stress the significance of the phytoplankton, a vertical phytoplankton model is developed, coupling the characteristics of phytoplankton dynamics with a physical model. Combining with the analyses of the Delft3D model, the vertical phytoplankton model is well applied to the Jiangsu coastal waters. Significant correlations are established between chlorophyll a and phytoplankton biomass. The vertical stability threshold is strongly related to the specific growth rate, the loss rate and the euphotic depth, and critical depth is closely linked with light availability. The vertical stability provides a favourable living condition for the phytoplankton, while the shallower mixed layer depth promotes the accumulation of phytoplankton density in the water volume. All the observed blooms falling within the predictions demonstrate that the physical limitation can produce reliable blooms. The findings of this study contribute much to the understanding of the coastal ecosystem and the coastal management.

Snow is one of the essential factors in hydrology, freshwater resources, irrigation, travel, pastimes, floods, avalanches, and vegetation. In this study, the snow cover of the northern and southern slopes of Alborz Mountains in Iran was investigated by considering two issues: (1) Estimating the snow cover area and the (2) effects of droughts on snow cover. The snow cover data were monitored by images obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The meteorological data (including the precipitation, minimum and maximum temperature, global solar radiation, relative humidity, and wind velocity) were prepared by a combination of National Centers for Environmental Prediction-Climate Forecast System Reanalysis (NCEP-CFSR) points and meteorological stations. The data scale was monthly and belonged to the 2000–2014 period. In the first part of the study, snow cover estimation was conducted by Multiple Linear Regression (MLR), Least Square Support Vector Machine (LSSVM), Group Method of Data Handling (GMDH), Multilayer Perceptron (MLP), and MLP with Grey Wolf Optimization (MLP-GWO) models. The most accurate estimations were produced by the MLP-GWO and GMDH models. The models produced better snow cover estimations for the northern slope compared to the southern slope. The GWO improved the MLP’s accuracy by 10.7%. In the second part, seven drought indices, including the Palmer Drought Severity Index (PDSI), Bahlme–Mooley Drought Index (BMDI), Standardized Precipitation Index (SPI), Multivariate Standardized Precipitation Index (MSPI), Modified Standardized Precipitation Index (SPImod), Joint Deficit Index (JDI), and Standardized Precipitation-Evapotranspiration Index (SPEI) were calculated for both slopes. The results showed that the effects of a drought event on the snow cover area would remain up to 5 (or 6) months in the region. The highest impact of drought appears after two months in the snow cover area, and the drought index most related to snow cover variations is the 2–month time window of SPI (SPI2). The results of both subjects were promising and the methods can be examined in other snowy areas of the world.

The Hailiutu River basin is a typical area of semi-arid sandy land with relatively flat topography, the surface of which is covered by undulating dunes and the development of the river system is not obvious. The dominant hydrological cycle is precipitation infiltration through dunes to aquifers followed by discharge to rivers. Therefore, the baseflow is an important component of the streamflow in this basin, but few studies for the baseflow characteristics have been conducted. The isotope tracer technique was applied to investigate the contributions of groundwater, soil water, and surface water to streamflow during the flood period. The results showed that the contributions of these components to streamflow were approximately 70%, 27%, and 3% respectively. Several automatic baseflow separation methods including filtering and recursive digital filtering (RDF) techniques were adopted to separate the baseflow from the streamflow and the adaptabilities of these methods were evaluated. All the filtering methods including Hydrograph Separation Program (HYSEP) and UK Institute of Hydrology’s method (UKIH) clearly underestimated the baseflow when compared with the standard baseflow results which were provided by the previous study using the tracer-based method in this basin, while the recursive digital filtering with Eckhardt filter technique (RDF-E) produced better performance. In the nonprecipitation period, the RDF-E method misidentified quick flow values which caused deviations between the separated baseflow and the above standard value. Hence, we proposed a modified automatic baseflow separation method called RDF-M by introducing the precipitation information into RDF-E. In comparison with the above standard, the RDF-M method provided similar baseflow results which were consistent with the actual situation of the Hailiutu River basin.

For social and economic development in China, the rivers are regulated, but its impact on river ecosystem has not evaluated yet. Maintaining a trade-off between social protection and ecological protection of the rivers is very crucial for a country where competition for water is severe. Thus, the main focus of this study is to carry out a comprehensive assessment of river health in relation to water resources management. In this paper, Guijiang, Nenjiang, and lower Yellow- three regulated river’s health statuses were drawn on the river-section scale using Fuzzy Matter Element (FME), Harmony Degree Evaluation (HDE), and Grey Relational Analysis (GRA) methods. The results indicated that the alteration of flow dynamics affected the ‘hydrology’ and ‘water ecology’ attributes of the rivers for which the River Health Index (RHI) of upstream sections of Nenjiang River was poor and downstream sections of lower Yellow River was critical respectively and the overall health statuses of the three rivers in that order, were good, fair to good, and fair. Particularly, Xiaolandi reservoir regulation at lower Yellow River impaired the habitat of benthic macroinvertebrate population that made the ecosystem health critical. We compared the evaluation methods and the results of FME and HDE were found consistent while GRA produced some discrepancies in the outcome. Finally, we proposed a framework for river health assessment for interpretation and communication of results to the managerial level. Our findings suggest that the water resources management of these regulated rivers should be rationalized and special care should be taken for ‘water ecology’ health restoration of the lower Yellow River.

Soil temperature (ST) is considered as one of the crucial characteristics of soil affecting physical and chemical processes of soil, agricultural products, the optimal time for planting seeds, etc and land surface ecological system. Hence, estimating this parameter can play an important role in agricultural and hydrology engineering. In this study, soil temperature was estimated using multilayer perceptron (MLP) and hybrid models of multilayer perceptron with invasive weed optimization algorithm (MLP-IWO). For training and testing of intelligence models meteorological data such as air temperature, relative moisture, wind speed, sunshine hours with a ten-year statistical period during 2007-2017 related to the meteorological station of Van in Turkey were used. The results showed for nominated scenario that the MLP-IWO performed best with lowest root mean square error (RMSE) at 2.057 °C when compared with original MLP model with RMSE at 2.426 °C, the MLP-IWO model ranked first. With greatly reduced modeling error rate, the MLP-IWO is suggested as superior models in soil temperature modeling and may be extended to other aspect of soil science. Overall, new hybrid MLP-IWO methods showed the potential to estimate soil temperature from freely available meteorological data, which may help reduce costs and labour in the field and this model has provided as a high accurate model for estimation soil temperature.

As the most important indicator of the coastal ecosystem, various studies on phytoplankton have frequently been proposed in coastal waters. In this study, the statistical analysis of phytoplankton biomass (in terms of chlorophyll a) is performed in the Wadden Sea near Lauwersoog (53.4167° N, 6.2000° E), The Netherlands, determined by the 10-year's historical dataset from 2000 through 2009. Boxplot analysis is introduced to give insight in the seasonal variations of phytoplankton biomass and physical–chemical conditions. Annually, a big difference is found in chlorophyll a, showing that the maximum values occur in the months of April and July, and the minimum values occur in winter (December, January, and February). The phytoplankton biomass is positively correlated with the physical conditions (salinity, light intensity, and water temperature), and is negatively correlated with the nutrients (nitrate, ammonium, and silicate). Factor analysis distinguishes the driving variables that represent most of the total variance using three extraction methods (principal component analysis, unweighted least squares, and maximum likelihood). Nitrate dominates the main activity in the first rotated component/factor, and ammonium contributes the most in the second rotated component/factor. The findings of this study are significant for understanding the roles of the environmental conditions upon the phytoplankton variability, and for reducing the overlapping information to characterize the driving variables.

The BLOOM II model is applied in this study to interrogate the variation of phytoplankton biomass (in terms of chlorophyll a) at the Frisian Inlet (NL), located in the north of the Netherlands. The validation results of the model show that more than 90% of the comparisons between model results and observations have a good agreement, while only 3.1% are classified as poor. Chlorophyll a is significantly correlated with the ecological indicators of Si and NH4 by observational analysis. The modelled chlorophyll a is fitted well by a normal distribution function (μ = 3.95 mg m−3, σ = 2.96 mg m−3). Relationships between the modelled ecological indicators are investigated. Secchi depth as another important indicator is closely linked with chlorophyll a, PAR and K d . Moreover, particular attention is paid to the phytoplankton biomass in response to nutrient availability. The decrease rate of chlorophyll a is from 16.8% to 19.2% for N-reduction, 17.4%–21.3% for P-reduction and 21.7%–28.0% for both N- and P-reduction. In the presence of uncertainty, the improved prediction of chlorophyll a is derived by Bayesian Markov Chain Monte Carlo (BMCMC) in this study. The improved prediction of chlorophyll a concentrations vary from 0.147 to 15.34 mg m−3 within the 95% confidence interval. This study emphasises the use of an ecological model to predict the variation of phytoplankton biomass, and improves the prediction with the integration of uncertainty analysis.

The epididymis performs an important role in the maturation of spermatozoa including their acquisition of progressive motility and fertilizing ability. However, the molecular mechanisms that govern these maturational events are still poorly defined. Here we report that Clpsl2, a novel colipase homology, is exclusively expressed in the caupt epididymis and conserved in mammalian. Clpsl2 was secreted into the lumen and covered the acrosome region and principal piece of spermatozoa tail. And during epididymal transit, the binding rate between Clpsl2 protein and the spermatozoa gradually decreased. Though Clpsl2 had the highest identifies with pancreatic colipase (Clps), Clpsl2 lacked those conserved amino acids in pancreatic Clps that interacting with lipase, correspondingly, the recombinant Clpsl2 protein did not possess the Clps function such as promoting the hydrolysis of lipase to its substrate glycerine trioleate. However, sequence analysis showed that Clpsl2 has the potency to bind with lipid. Knockdown expression of Clpsl2 by lentivirus-mediated RNAi in vivo caused an attenuation of spermatozoa motility, a suppressed acrosomal reaction, a decrease of cauda spermatozoa number, and subfertility. This study identified a novel and conserved molecule, Clpsl2, was specifically expressed in epididymis and involved in the regulation of spermatozoa motility, acrosomal integrity, and male fertility.

In the original soil and water assessment Tool (SWAT) model (SWAT-O), the contributions of shallow aquifers and deep aquifers to streamflow are simulated using the linear reservoir method. The movement of groundwater was limited in the hydrological response unit which is a minimum calculation unit in the SWAT. However, this computational method may not be suitable for the areas where a groundwater system is complicated, and the river is predominately recharged by groundwater. In this paper, we proposed an enhanced groundwater module which divides shallow aquifers into upper and lower aquifers, integrates all the deep aquifers of a sub-basin into a regional aquifer, and simulates interactive water amount between lower aquifer and deep aquifer using water depth difference. The modified groundwater module was introduced to the original SWAT model, hereby referred to as SWAT-MG. The SWAT-MG and SWAT-O models were applied to the Hailiutu River catchment, which is a semi-arid wind sandy grass shoal catchment. Results showed that both models underestimated streamflow in peak flow, while the simulated streamflow of SWAT-MG was closer the observed values than that of SWAT-O. Three evaluation criteria (NSE, RSR, PBIAS) were applied to evaluate the performance of the models and the results showed that SWAT-MG had a better performance than SWAT-O. The baseflow index of Hailiutu River which was calculated by the results of SWAT-MG was 96.78%, which means the streamflow is predominately recharged by groundwater, and this conforms to the actual situation of Hailiutu River catchment. This indicates that a SWAT model with a modified groundwater module could better represent the groundwater flow behavior in the study area.

For sustainable water resource management, it is important to determine the relationship between streamflow and other variables that influence availability of water resources. However, many catchments do not have sufficient data to allow for a more detailed study of these relationships. We faced a similar challenge in the Chengcun Basin (limited historical data: from 1986–1999); and therefore we used a new approach to overcome this. We found that, using nonparametric trend methods in conjunction with the climate elasticity analyses and the 2D visualization of hydrologic data, it is possible to assess the relationships between streamflow and other hydro-climatic variables. In the past, streamflow trends in the basin were more correlated with precipitation than with potential evapotranspiration (PET). In addition, there is a gradual shift in the hydrological regime of the catchment, which may affect the occurrence of available water resources and activities that depend on them. In addition, based on our climate sensitivity analyses, the streamflow is dependent and more sensitive to variations in precipitation than to PET (δQ=0.79δP+0.42δE; precipitation elasticity, ɛP =1.32; PET elasticity, ɛE =−2.10). Therefore pending more detailed studies, the use of our approach will provide a rapid means to assess the variation of water resources (streamflow) in a watershed. In the future, we hope to carry out related research in other watersheds and also perform a more detailed studies to improve upon the results of this study.

Soil erosion, more than any other factor, has more significant and direct impacts on a hydrological watershed in terms of environmental problems. After deriving the erosion hotspots, this s...

The Xinanjiang rainfall–runoff model has been successfully applied in many humid and sub-humid areas in China since 1973. The wide application is due to the simple model structure, the clear physical meaning of the parameters and the well-defined model calibration procedure. However, due to a data scarcity problem and short runoff concentration time, its applications to small drainage basins are difficult. Therefore, we investigate the model application in Lianghui, a small drainage basin of Zhejiang province in China. By using generalized likelihood uncertainty estimation (GLUE) methodology, the sensitivity of parameters of Xinanjiang model was investigated. The data clearly showed that equifinality phenomenon was evident in both water balance parameter calibration and runoff routing parameter calibration procedures. The results showed that K (evapotranspiration conversion coefficient), C s (recession constant in channel system) and S m (areal free water storage capacity of surface soil) are the most sensitive parameters for the water balance parameter calibration while C s , S m and W m (mean area tension water capacity) are the most sensitive parameters for runoff routing parameter calibration. The conclusion is favourable for understanding parameters of Xinanjiang model in order to provide valuable scientific information for simulating hydrological processes in small drainage basins.

The Hailiutu River basin is a typical semi-arid wind sandy grass shoal watershed in northwest China. Climate and land use have changed significantly during the period 1970&ndash, 2014. These changes are expected to impact hydrological processes in the basin. The Mann&ndash, Kendall (MK) test and sequential t-test analysis of the regime shift method were used to detect the trend and shifts of the hydrometeorological time series. Based on the analyzed results, seven scenarios were developed by combining different land use and/or climate situations. The Soil Water Assessment Tool (SWAT) model was applied to analyze the impacts of climate variability and land use change on the values of the hydrological components. The China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) was applied to enhance the spatial expressiveness of precipitation data in the study area during the period 2008&ndash, 2014. Rather than solely using observed precipitation or CMADS precipitation, the precipitation values of CMADS and the observed precipitation values were combined to drive the SWAT model for better simulation results. From the trend analysis, the annual streamflow and wind speed showed a significant downward trend. No significant trend was found for the annual precipitation series, however, the temperature series showed upward trends. With the change point analysis, the whole study period was divided into three sub-periods (1970&ndash, 1985, 1986&ndash, 2000, and 2001&ndash, 2014). The annual precipitation, mean wind speed, and average temperature values were 316 mm, 2.62 m/s, and 7.9 &deg, C, respectively, for the sub-period 1970&ndash, 1985, 272 mm, 2.58 m/s, and 8.4 &deg, C, respectively, for the sub-period 1986&ndash, 2000, and 391 mm, 2.2 m/s, and 9.35 &deg, C, respectively, for the sub-period 2001&ndash, 2014. The simulated mean annual streamflow was 35.09 mm/year during the period 1970&ndash, 1985. Considering the impact of the climate variability, the simulated mean annual streamflow values were 32.94 mm/year (1986&ndash, 2000) and 36.78 mm/year (2001&ndash, 2014). Compared to the period 1970&ndash, 1985, the simulated mean annual streamflow reduced by 2.15 mm/year for the period 1986&ndash, 2000 and increased by 1.69 mm/year for the period 2001&ndash, 2014. The main variations of land use from 1970 to 2014 were the increased area of shrub and grass land and decreased area of sandy land. In the simulation it was shown that these changes caused the mean annual streamflow to decrease by 0.23 mm/year and 0.68 mm/year during the periods 1986&ndash, 2000 and 2001&ndash, 2014, respectively. Thus, the impact of climate variability on the streamflow was more profound than that of land use change. Under the impact of coupled climate variability and land use change, the mean annual streamflow decreased by 2.45 mm/year during the period 1986&ndash, 2000, and the contribution of this variation to the decrease in observed streamflow was 27.8%. For the period 2001&ndash, 2014, the combined climate variability and land use change resulted in an increase of 0.84 mm/year in annual streamflow. The results obtained in this study could provide guidance for water resource management and planning in the Erdos plateau.

The impact of the El Nino-Southern Oscillation (ENSO) phenomenon within the Huangfuchuan watershed, one of the major first- order sub-basins in th e middle region of the Yellow River, has not clearly been established. Consequently, the co-varying relationships between rainfall and El Nino/La Nina spanning the period 1954 to 2010 are investigated. Trends and step changes in annual rainfall are investigated with the Mann-Kendall and the distribution free cumulative sum (CUSUM) tests. Wavelet transfor ms are employed to perform spectral analysis of temporal variations in rainfall rates within the watershed. Cross wavelet and wavelet coherence transforms are used to study localized co-varying relationships between rainfall and ENSO index. Results from statistical tests indicate that rainfall in the Huangfuchuan watershed is declining, although not significantly. In addition, wavelet coherency and cross wavelet analysis, and comparison of the extracted dominant annual rainfall and 2–7 year ENSO signals demonstrate that ENSO events impact Huangfuchuan rainfall with El Nino corresponding to rainfall de cline and La Nina to rainfall increment with a semiannual to annual lag.

Investigating the influences of soil characteristic factors on dinoseb adsorption parameter with different statistical methods would be valuable to explicitly figure out the extent of these influences. The correlation coefficients and the direct, indirect effects of soil characteristic factors on dinoseb adsorption parameter were analyzed through bivariate correlation analysis, and path analysis. With stepwise regression analysis the factors which had little influence on the adsorption parameter were excluded. Results indicate that pH and CEC had moderate relationship and lower direct effect on dinoseb adsorption parameter due to the multicollinearity with other soil factors, and organic carbon and clay contents were found to be the most significant soil factors which affect the dinoseb adsorption process. A regression is thereby set up to explore the relationship between the dinoseb adsorption parameter and the two soil factors: the soil organic carbon and clay contents. A 92% of the variation of dinoseb sorption coefficient could be attributed to the variation of the soil organic carbon and clay contents.

Sorption and degradation behaviors of atrazine were investigated with packed and intact soil columns. The measured breakthrough curves from columns were fitted with CXTFIT model program. The high mobility and long persistence of atrazine indicated by experiment results confirm that atrazine application may cause potential groundwater pollution. It was found that the obtained sorption and degradation parameters from the packed column experiments were quite similar to those obtained from the intact column experiments. These results suggest that sorption and degradation behaviors in both experiments can be used jointly to model herbicide transport at a large scale if the soil is relatively homogeneous.

Two-dimensional dispersion model (DISP2D), based on the stream tube concept, has been developed to predict the depth averaged concentration distribution in channels under steady flow conditions. Such water quality model is not feasible for the practical engineering use for channels in case of lacking enough hydraulic data. In this paper, it is intended to couple the DISP2D model with one hydrodynamic model to make the model powerful and feasible. The proposed hydrodynamic model is based on the depth averaged longitudinal momentum equation. To solve the hydrodynamic model numerically, some assumptions have been made to take into account the effect of secondary currents. The flow model was tested using data on prismatic flumes with compound cross-sections. The integrated hydrodynamic and dispersion model was further examine by using the measured velocity and concentration data. Comparison shows that the predicted lateral flow and concentration distributions are in good agreement with the measured data. Successful applications indicate that the proposed model can be used for simulating flow and contaminant transport in open channels.

A two dimensional(2D) numerical model based on the stream tube concept is developed not only to quantitatively examine the physical behavior in open channels, but also for examining the physical processes release of pollutants upstream. It is also intended to examine the numerical property advection scheme combined with the flux limiter. The proposed numerical model is examined by comparing with both the analytical solution and measurements. The model performance is discussed and the results are analyzed. Both the numerical test and the model application show that the proposed model is capable for modeling two dimensional contaminant transports in open channel flows. The advantage of the proposed model is that it requires less hydraulic data and is ease of use. Therefore it is of great practical interest for engineering use.